Autonomous exploration in unknown environments is a critical capability for multi-UAV systems. However, existing methods often suffer from unbalanced task allocation, low exploration efficiency, and unstable paths, especially in large-scale and complex scenarios. To address these challenges, this paper presents an adaptive coordination exploration approach for multi-UAV systems. In the proposed approach, dynamic region allocation, entropy-guided local planning, and direction-consistent frontier selection are integrated to achieve efficient and collaborative exploration. The system first partitions the environment adaptively based on workload and regional complexity. It then prioritizes high-information-value areas for exploration. Directional constraints are further applied to improve path continuity and reduce turning redundancy. Extensive experiments in indoor maze and pillar environments, and outdoor forest and urban environments demonstrate that the proposed approach outperforms state-of-the-art baselines. Furthermore, ablation studies validate the necessity and complementarity of each module. This work provides a practical and efficient solution for multi-UAV exploration in structured and unstructured environments.

Constrained consensus formation tracking of autonomous underwater vehicle (AUV) networks is a challenging problem to solve, especially when the networks are possibly subject to nonuniform, time-varying communication delays and marine disturbances. This article presents a systematic design framework to achieve formation objectives while ensuring network stability under such uncertainties. First, a coordinate transformation is applied to the AUV kinematics to address nonholonomic constraints. A distributed consensus protocol is then used to coordinate the motion of vehicles, and utilizing the transformed kinematic model, the desired linear velocity and approach angles are determined accordingly. By employing the graph representation and Lyapunov-Krasovskii functional method, a robust stability criterion is derived in terms of linear matrix inequalities (LMIs) for a delayed network with disturbances. To improve the quality of AUV motion control, on top of the conventional backstepping controller, a sequential optimization procedure is developed for the first time, which enables optimizing the robust performance online while respecting motion constraints. Moreover, the overall stability of the resulting formation system is established. Finally, comparative simulations are carried out to verify the effectiveness and superiority of the proposed method.

Formation control of unmanned underwater vehicles (UUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for UUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between UUVs, are particularly suitable for underwater environments. However, traditional rigid graph-based formation control methods often face challenges such as control jumps and weak robustness in dynamic underwater environments. To address these challenges, a novel rigid graph-based model predictive controller (RGMPC) is proposed in this paper. The proposed approach integrates model predictive control with traditional rigid graph-based methods, effectively avoiding issues such as control jumps and thrust saturation while enhancing robustness. Moreover, constraints are constructed using backstepping methods to theoretically ensure the closed-loop stability of the algorithm. Finally, the algorithm is validated through extensive simulations based on mathematical models and further tested in the Gazebo simulator, fully demonstrating its feasibility and effectiveness.

This article presents a novel knowledge-based genetic algorithm (GA) to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed algorithm introduces a unique and straightforward representation of the robot path and an effective method for evaluating the path quality and accurately detecting collisions. The proposed algorithm is capable of finding optimal or suboptimal robot paths in both static and dynamic environments. Simulation and experimental studies are conducted to showcase the effectiveness and efficiency of the proposed algorithm. Furthermore, a comparative study is performed to highlight the indispensable role of specialized genetic operators within the proposed algorithm in solving the path planning problem.

Flocking control, as an essential approach for survivable navigation of multirobot systems, has been widely applied in fields, such as logistics, service delivery, and search and rescue. However, realistic environments are typically complex, dynamic, and even aggressive, posing considerable threats to the safety of flocking robots. In this article, based on deep reinforcement learning, anAsymmetricSelf-play-empoweredFlockingControl framework is proposed to address this concern. Specifically, the flocking robots are trained concurrently with learnable adversarial interferers to stimulate the intelligence of the flocking strategy. A two-stage self-play training paradigm is developed to improve the robustness and generalization of the model. Furthermore, an auxiliary training module regarding the learning of transition dynamics is designed, dramatically enhancing the adaptability to environmental uncertainties. Feature-level and agent-level attention are implemented for action and value generation, respectively. Both extensive comparative experiments and real-world deployment demonstrate the superiority and practicality of the proposed framework.

Multirobot flocking is crucial for safe and cooperative navigation, with wide applications in logistics, service delivery, and mobile surveillance. Despite significant progress, developing effective flocking strategies under complex conditions remains challenging. Communication is a vital technique for multirobot coordination. In this article, we propose refinement and enhancement of communication information (REIN), a novel deep reinforcement learning-based framework designed to improve communication effectiveness in leader–follower flocking systems through the REIN. First, regarding information refinement, a graph-based information refiner, integrating directed graph-structured communication with an innovative edge filter, is developed for selective multirobot interaction. It helps robots adaptively focus on relevant neighbors, considerably alleviating information overload. Second, for information enhancement, a cognition-aligned information enhancer is designed that boosts information expressiveness by encouraging team consensus. It utilizes two cascaded leader-related objectives to optimize information towards cognitive alignment among decentralized followers. Extensive comparisons with state-of-the-art approaches and ablation versions demonstrate the superiority of our framework. Physical experiments are also conducted to validate its practicality.

Fish schools present high-efficiency group behaviors to collective migration and dynamic escape from the predator through simple individual interactions. The purpose of this research is to infuse swarm robots with "fish-like" intelligence that will enable safe navigation and efficient cooperation, and successful completion of escape tasks in changing environments. In this paper, a novel fish-inspired self-adaptive approach is proposed for the collective escape of swarm robots. A bio-inspired neural network (BINN) is introduced to generate collision-free escape trajectories through the dynamics of neural activity and the combination of attractive and repulsive forces. In addition, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in dynamic environments. Similar to fish escape maneuvers, simulations and real-robot experiments show that the swarm robots can collectively leave away from the threat and respond to sudden environmental changes. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness, efficiency, and flexibility of swarm robots in complex environments.

Deep-learning-based object detection methods have achieved significant results in unmanned aerial vehicle (UAV) crop seedling image detection. However, when there are differences in the shape characteristics and sizes of seedlings within datasets, the performance of the detector tends to decrease. Existing methods typically rely on specific datasets, ignoring the problem of feature disparities caused by complex and variable field environments. In this letter, a cotton seedling detection framework based on cross-scale feature enhancement (CFE) is presented. CFE reconstructs features through multilevel feature aggregation (MFA) and enhances the reconstructed feature layers using global contextual dependencies extracted by transformer encoder, enabling the sharing of long-range dependency information across different feature spaces. Furthermore, a fuzzy dynamic weighted loss (FDWLoss) strategy is proposed to balance the targets for difficult-to-identify in the training process. Experimental results demonstrate a significant improvement in detection performance and generalization ability on six datasets of the proposed model, which is particularly suitable for cotton seedling detection in various field environments.

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle’s physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional FTCs are presented, thus verifying the effectiveness and robustness of the proposed GOA-based fault-tolerant trajectory tracking design. Note to Practitioners—This paper is motivated by the actuator saturation problem that exists in the trajectory tracking of an underwater vehicle (UV) when encountering power loss of the thruster system. The fault-tolerance trajectory tracking performance is affected by physical constraints of the vehicle when using the traditional methods as they may deduct excessive kinematic/dynamic requirements during the control process, thus inducing the deviation of the tracking trajectory. Therefore, the refined backstepping as well as the grasshopper optimization (GOA) are combined to eliminate the excess, where the refined backstepping is used to alleviate the speed jumps (kinematic outputs) and the GOA is to control the propulsion forces (dynamic outputs) when facing thruster fault cases. This innovates the industrial practitioners that the control design of the vehicle can be improved to avoid the tracking deviation brought by unsatisfied driving commands under fault cases through embedding optimization algorithms. Moreover, for the specific type of UV studied in this paper used for dam detection, simulations regarding practical dam detection such as the 3D polygonal line trajectory tracking and the frequently occurring UV single-fault cases are chosen, which can serve as references for practitioners working in the related field. In the future, underwater experiments of the UV will be investigated, with more effects of the practical environment involved.

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

This article investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.

Multi-robot cooperative navigation is an important task, which has been widely studied in many fields like logistics, transportation, and disaster rescue. However, most of the existing methods either require some strong assumptions or are validated in simple scenarios, which greatly hinders their implementation in the real world. In this paper, more complex environments are considered in which robots can only acquire local observations from their own sensors and have only limited communication capabilities for mapless collaborative navigation. To address this challenging task, we propose a hierarchical framework, by fusing bothSensor-wise andAgent-wise features forPerception-Improving (SAPI), which can adaptively integrate features from different information sources to improve perception capabilities. Specifically, to facilitate scene understanding, we assign prior knowledge to the visual coder to generate efficient embeddings. For effective feature representation, an attention-based sensor fusion network is designed to fuse sensor-level information of visual and LiDAR sensors, while graph convolution with multi-head attention mechanism is applied to aggregate agent-level information from an arbitrary number of neighbors. In addition, reinforcement learning is used to optimize the policy, where a novel compound reward function is introduced to guide training. Extensive experiments demonstrate that our method has excellent generalization ability in different scenarios and scalability for large-scale systems.

The paper presents an innovative approach (CBNNTAP) that addresses the complexities and challenges introduced by ocean currents when optimizing target assignment and motion planning for a multi-unmanned underwater vehicle (UUV) system. The core of the proposed algorithm involves the integration of several key components. Firstly, it incorporates a bio-inspired neural network-based (BINN) approach which predicts the most efficient paths for individual UUVs while simultaneously ensuring collision avoidance among the vehicles. Secondly, an efficient target assignment component is integrated by considering the path distances determined by the BINN algorithm. In addition, a critical innovation within the CBNNTAP algorithm is its capacity to address the disruptive effects of ocean currents, where an adjustment component is seamlessly integrated to counteract the deviations caused by these currents, which enhances the accuracy of both motion planning and target assignment for the UUVs. The effectiveness of the CBNNTAP algorithm is demonstrated through comprehensive simulation results and the outcomes underscore the superiority of the developed algorithm in nullifying the effects of static and dynamic ocean currents in 2D and 3D scenarios.

In this article, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply–demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely, the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep$Q$-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to interhour and interday power fluctuation and to compare its performance with those of the benchmark algorithms.

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization.

For people who ardently love painting but unfortunately have visual impairments, holding a paintbrush to create a work is a very difficult task. People in this special group are eager to pick up the paintbrush, like Leonardo da Vinci, to create and make full use of their own talents. Therefore, to maximally bridge this gap, we propose a painting navigation system called “Angle’s Eyes” to assist blind people in artistic creation. The proposed system is composed of cognitive system and guidance system. The system adopts drawing board positioning based on QR code, brush navigation based on target detection and bush real-time positioning. Meanwhile, we design a simple yet efficient position information coding rule to remind the user of the current brush tip position. In addition, we design a criterion to efficiently judge whether the brush reaches the target or not. The numerous experiments are conducted to optimize and test the performance of the system. The results of real-world scenario experiments demonstrate that the developed system has great potential to help blind people with painting. This work also demonstrates that it is practicable for the blind people to feel the world through the brush in their hands. In the future, we plan to deploy “Angle’s Eyes” on the phone to make it more portable. The demo video of the proposed painting navigation system is available athttps://doi.org/10.6084/m9.figshare.9760004.v1.

Accurate evaluation of orchard areas from remote sensing images is of great importance in economic and ecological aspects. In practice, the differences in distributions between remote sensing images and the lack of data labels make the semantic segmentation model impossible to use in new data. Unsupervised domain adaptation (UDA) methods can improve the performance of the model in the target domain by aligning the source domain and the target domain. However, due to the class mismatch problem and the interference of high-dimensional feature complexity, most UDA methods cannot achieve satisfactory results in orchard areas segmentation task. To address these issues, we propose an UDA model for orchard areas segmentation by developing a class feature aggregate discriminator. The class feature aggregate discriminator is designed to distinguish intra-domain classes and align inter-domain classes, and class feature aggregate can represent class information of different domains, which helps the model to avoid the interference of complex information. In addition, adversarial loss reweighting is introduced to the novel model, which makes the segmentation model pay more attention to the orchard areas. To verify the effectiveness of the proposed method, we conducted extensive experiments in three different remote sensing images around Yichang City. Compared to the baseline model, the proposed approach improves IoU by 27.68%, and we achieve high gains of 6.07% in IoU over other UDA methods. The larger gain indicates that our proposed method has great potential in cross-domain orchard areas segmentation.

One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control voltage, feedback current, and rotational speed of the deep-sea HOV thruster; and the output is the corresponding fault degree parameter ( s ), which indicates the pattern and degree of the thruster fault. In order to illustrate the effectiveness of the proposed fault diagnosis method, a pool experiment under different simulated fault cases is conducted in this study. The experimental results have proved that the DBN information fusion fault diagnosis method can not only diagnose the continuously changing, uncertain, and unknown thruster fault but also has higher identification accuracy than the information fusion fault diagnosis methods based on traditional artificial neural networks.

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this article presents an intelligent trust cloud management method. First, an active training mechanism is proposed to construct the standard trust clouds. Second, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Third, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.

This paper presents a neural network-based approach for the path planning of arbitrary shaped mobile robots in complex environments, with the consideration of safety. A 2D workspace is discretized to a topologically organized map using a biological neural network, in which the dynamic neural activity landscape represents the environmental information. A set of kernel matrices are established to describe the shape and orientation features of the robot. Taking the safety factor into consideration, the translation and rotation performances of the robot on each neuron node of the workspace are determined using a convolutional neural network (CNN). Then, from the initial state of the robot to the target state, a node rooted tree is constructed by searching the adjacent neurons, and the moving path of the robot is generated by backward searching the node rooted tree. By changing the bias coefficient in the convolutional calculation, the clearance between the planned path and the obstacles can be conveniently adjusted. The effectiveness of the proposed method is demonstrated through several simulations conducted in both static and dynamic environments. The results show that the method can effectively solve the “path blocked” issue caused by small densely scattered obstacles, and also solve the “too close” and “too far” path planning problems.

Research on tracking control has been on-going for many years. The accuracy and the practicality of the tracking control method has always been one of the most important aspects when designing the control strategy. Autonomous Underwater Vehicles are becoming increasingly important in the applications of ocean surveillance and military, etc. Therefore, this paper aims to develop a control method for autonomous underwater vehicles based on bioinspired neural dynamics. The proposed method practically solves the speed jump and chattering issues that are respectively in conventional backstepping and sliding mode controls with the aid of the bioinspired neural dynamics. In addition, the proposed control method also takes the dynamic uncertainties for the autonomous underwater vehicle into the consideration. The combined tracking method has relatively good overall performance for autonomous underwater vehicle against model uncertainties and disturbances.

This paper presents a low-cost and unobtrusive intelligent respiratory monitoring system. To achieve low-cost and remote measurement of respiratory signal, an RGB camera collaborated with marker tracking is used as data acquisition sensor, and a Raspberry Pi is used as data processing platform. To overcome challenges in actual applications, the signal processing algorithms are designed for removing sudden body movements and smoothing the raw signal. To discover more specific information in the respiratory signal, respiratory rate is estimated by a translational cross point algorithm, and respiratory pattern is identified by recurrent neural network. Finally, the obtained decision-making information and some original information are sent to user's smartphone via a cloud service platform. For estimating respiratory rate, the Bland-Altman plot demonstrates the satisfactory results with agreement ranges of -0.13 ± 5.85 bpm. With respect to the classification of breathing patterns, the results validate that the system has the good performance with the accuracy, precision, recall, and F1 of 92.5%, 92.5%, 93.3%, and 92.9%, respectively. This work may contribute to the development of low-cost and non-contact respiratory monitoring products specific to home or work health care.

The aim of this study is to develop an affordable and remote intelligent respiratory monitoring system. To achieve low-cost and remote measurement of respiratory signal, an RGB camera collaborated with marker tracking is used as a data acquisition sensor, and a Raspberry Pi is used as a data processing platform. To overcome challenges in actual applications, the signal processing algorithms are designed for removing sudden body movements and smoothing the raw signal. Subsequently, respiratory rate (RR) is estimated by a translational cross-point algorithm, and the respiratory pattern is identified by the recurrent neural network. For estimating RR, the translational cross-point algorithm performs better than other methods with root-mean-square error (RMSE) of 3.29 bpm. With respect to the classification of breathing patterns, the established neural network performs better than support vector machine-based classifiers with the accuracy, precision, recall, and F1 of 89.0%, 89.0%, 90.5%, and 89.0%, respectively. The obtained decision-making information and some original information are sent to the user’s smartphone via a cloud service platform. In a way, due to its low-price, noncontact, and portable merits, the established system can be seen as a “respiratory consultant” by your side.

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in recent years, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many existing MKGC methods design a fat model that poses challenges for computational cost and clustering performance, as they learn both an affinity graph and an extra consensus kernel cumbersomely. To tackle this challenging problem, this article proposes a new MKGC method to learn a consensus affinity graph directly. By using the self-expressiveness graph learning and an adaptive local structure learning term, the local manifold structure of the data in kernel space is preserved for learning multiple candidate affinity graphs from a kernel pool first. After that, these candidate affinity graphs are synthesized to learn a consensus affinity graph via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- k neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes. The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods.

Security is one of the major concerns in industrial wireless sensor networks (IWSNs). To assure the security in clustered IWSNs, this article presents a secure clustering protocol with fuzzy trust evaluation and outlier detection. First, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then, a density-based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy-based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.

This article is concerned with the issues of finite-time control for a class of continuous-time nonlinear semi-Markovian jump systems (SMJSs) with saturation constraint via observed-based control. Both sensor and actuator saturations, and unknown nonlinearities are considered simultaneously. The main purpose of this article is to derive parameter selection sufficient conditions by designing an observed-based controller. These conditions ensure that the system is finite-time boundedness (FTB). By employing some reasonable assumptions and constructing an appropriate semi-Markovian Lyapunov function, some novel finite-time stabilization criteria are obtained, which guarantee the FTB for the underlying systems over the whole finite-time interval. Meanwhile, the observed-based controller is designed. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

Multiple mobile robots in formation are often required to dock to each other to overcome the limitations, such as battery failure, transportation capacity, and maneuverability on rough terrains; however, it is challenging to design a single controller that navigates the robots to dock to each other, maintains the other robots in formation, and is applicable to both docked and nondocked robots, while it is also robust to uncertainties and disturbances. This article proposes a novel robust subsumption architecture for nonholonomic mobile robots in formation with docking capability. In addition to docking, the robots, i.e., all the nondocked robots and the front-docked robots, maintain a formation that can also be switched automatically to other configurations when necessary and avoid collisions with other robots and dynamic obstacles. The proposed subsumption control architecture takes into account each follower's desired goal as well as its docking condition to synthesize a control law as a velocity control signal that is then used to determine the robust input torque for each follower using the robots' dynamics. The Lyapunov stability of the controller is also proved. We also develop strategies for efficient centralized motion planning of the followers to achieve various goals, e.g., formation keeping/switching, docking, and collision avoidance. The effectiveness of our proposed methodology was verified in simulations as well as implementations on a virtual robot environment. Note to Practitioners - Multiple mobile robots, especially when operating as a formation, are able to perform tasks that are beyond the capabilities of individual robots. Existing formation control approaches neglect some realistic limitations of mobile robots, such as battery failure, limited transportation capacity, and maneuverability, to name a few. This article was motivated by these realistic limitations of mobile robots when operating in formation, and it suggests a new approach for navigation of such robots by docking some (or all) of these robots to each other and pursue a variety of goals. The goal includes autonomous docking, formation keeping/switching, and collision avoidance in dynamic environments. We include robot dynamics and system uncertainties in our algorithm and provide a robust control methodology. Therefore, the developed methodologies in this article can be adopted in real applications that require robots to be supplied with sufficient battery or having a large payload capacity, e.g., agricultural robotics.

In this paper, controlling miniature swimmers via electromagnetic actuation has received considerable attention due to their minor invasive trait in various biomedical applications and ease of passing through the complex environments. Studying the navigation and control system is an essential step towards such applications. Currently, navigation and control for magnetically driven miniature swimmers are still challenging research issues. This paper aims to formulate a navigation and control system of magnetically driven helical miniature swimmers. First, a global planning algorithm named informed optimal random exploring tree (Informed RRT*) is applied to compute the feasible path in cluttered environments. Second, a closed-loop control algorithm is presented to follow various of reference paths using visual feedback on a planar substrate. In particular, a single-hidden layer feedforward neural networks is employed to approximate the mapping relationship between the magnetic self-rotation direction and the actual moving direction of helical miniature swimmers. The neural network is first implemented to control the magnetically driven miniature swimmers in this paper. Experiments are conducted to verify the ability of navigation and visual feedback control tasks.

Average consensus theory is intensely popular for building time synchronization in wireless sensor network (WSN). However, the average consensus-based time synchronization algorithm is based on the iteration that poses challenges for efficiency, as they entail high communication cost and long convergence time in large-scale WSN. Based on the suggestion that the greater the algebraic connectivity the faster the convergence, a novel multihop average consensus time synchronization (MACTS) is developed with innovative implementation in this article. By employing multihop communication model, it shows that virtual communication links among multihop nodes are generated and algebraic connectivity of the network increases. Meanwhile, a multihop controller is developed to balance the convergence time, accuracy, and communication complexity. Moreover, the accurate relative clock offset estimation is yielded by delay compensation. Implementing the MACTS based on the popular one-way broadcast model and taking multihop over short distances, we achieve hundreds of times the MACTS convergence rate compared to average TimeSync (ATS).

Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this article, a rapid-flooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases.

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering (NLMSF), coarse segmentation using k -means clustering, and fine segmentation using an improved region-scalable fitting (RSF) model. The NLMSF is used before the segmentation to effectively remove speckle noise while preserving meaningful details such as edges and fine features, which can make the segmentation easier and more accurate. After despeckling, a coarse segmentation is obtained by using k -means clustering, which can reduce the number of iterations. In the fine segmentation, to better deal with possible intensity inhomogeneity, an edge-driven constraint is combined with the RSF model, which can not only accelerate the convergence speed but also avoid trapping into local minima. The proposed method has been successfully applied to both noisy and inhomogeneous sonar images. Experimental and comparative results on real and synthetic sonar images demonstrate that the proposed method is robust against noise and intensity inhomogeneity, and is also fast and accurate.

Intending to mimic the operating mechanism of biological neural systems, a self organizing map-based approach to task assignment of a swarm of robots in 3-D dynamic environments is proposed in this paper. This approach integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of a swarm of robots in 3-D environments under uncertain situations, such as when some robots are presented in or broken down or when more than one robot is needed for some special task locations. A Bezier path optimizing algorithm and a parameter adjusting algorithm are integrated in this paper. It is capable of reducing the complexity of the robot navigation control and limiting the number of convergence iterations. The simulation results with different environments demonstrate the effectiveness of the proposed approach.

This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.

Cooperative exploration in unknown environments is fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are the key issues for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish target searching and foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance module improves the smoothness of robot trajectory. In the simulation studies, several scenarios with and without the fuzzy module are investigated. The robot trajectory smoothness improvement is demonstrated through the comparative studies.

This paper proposes an efficient wayfinding strategy for robot navigation in regionalized environments by designing a regionalized spatial knowledge model (RSK model) and a region-based wayfinding algorithm, i.e., a fine-to-coarse A* (FTC-A*) search algorithm. First, the RSK model, which imitates the representation of environments in the human brain, is presented to describe the search environments. The environments that are divided into regions are represented by a hierarchical nested structure where small regions are grouped together to form superordinate regions. Second, on the basis of the RSK model, an FTC-A* search algorithm is developed to plan the fine-to-coarse route. By making a fine planning to robot surroundings in vicinity, but a coarse planning to that at the distance, the FTC-A* algorithm can effectively reduce computational complexity, so as to enhance the efficiency of route search, and meanwhile makes robots to react quickly to user's commands, especially in large-scale environments. Finally, four exhaustive simulations and a physical experiment have been carried out to illustrate the feasibility and effectiveness of the proposed wayfinding strategy.

Target search in 3-D underwater environments is a challenge in multiple autonomous underwater vehicles (multi-AUVs) exploration. This paper focuses on an effective strategy for multi-AUV target search in the 3-D underwater environments with obstacles. First, the Dempster-Shafer theory of evidence is applied to extract information of environment from the sonar data to build a grid map of the underwater environments. Second, a topologically organized bioinspired neurodynamics model based on the grid map is constructed to represent the dynamic environment. The target globally attracts the AUVs through the dynamic neural activity landscape of the model, while the obstacles locally push the AUVs away to avoid collision. Finally, the AUVs plan their search path to the targets autonomously by a steepest gradient descent rule. The proposed algorithm deals with various situations, such as static targets search, dynamic targets search, and one or several AUVs break down in the 3-D underwater environments with obstacles. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve search task of multiple targets with higher efficiency and adaptability compared with other algorithms.

A novel biologically-motivated neural networks approach associated with developed vector-driven autonomous robot navigation is proposed in this paper. The biologically-motivated neural networks (BNN) algorithm is employed to guide an autonomous robot to reach goal with obstacle avoidance motivated by Grossberg's model for a biological neural system. As the robot plans its trajectory toward the goal, unreasonable path will be inevitably planned. A vector-based guidance paradigm is developed for guidance of the robot locally so as to plan more reasonable trajectories. In addition, square cell map representations are proposed for realtime autonomous robot navigation. The BNN based scheme demonstrates that the algorithms avoid the issue of local minima in path planning. In this paper, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter collision-free paths in non-stationary and unstructured environments compared with other approaches.

In this paper, we investigate the problem of robust stability for a class of delayed neural networks of neutral-type with linear fractional uncertainties. The activation functions are assumed to be unbounded, non-monotonic and non-differentiable, and the delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of the interval time-varying delay are available. By constructing a general form of the Lyapunov–Krasovskii functional, and using the linear matrix inequality (LMI) approach, we derive several delay-dependent stability criteria in terms of LMI. Finally, we give a number of examples to illustrate the effectiveness of the proposed method.

Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved.

Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. The simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments.

Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult for API algorithms to obtain near-optimal policies for Markov decision processes (MDPs) with large or continuous state spaces. To address this problem, this paper presents a hierarchical API (HAPI) method with binary-tree state space decomposition for RL in a class of absorbing MDPs, which can be formulated as time-optimal learning control tasks. In the proposed method, after collecting samples adaptively in the state space of the original MDP, a learning-based decomposition strategy of sample sets was designed to implement the binary-tree state space decomposition process. Then, API algorithms were used on the sample subsets to approximate local optimal policies of sub-MDPs. The original MDP was decomposed into a binary-tree structure of absorbing sub-MDPs, constructed during the learning process, thus, local near-optimal policies were approximated by API algorithms with reduced complexity and higher precision. Furthermore, because of the improved quality of local policies, the combined global policy performed better than the near-optimal policy obtained by a single API algorithm in the original MDP. Three learning control problems, including path-tracking control of a real mobile robot, were studied to evaluate the performance of the HAPI method. With the same setting for basis function selection and sample collection, the proposed HAPI obtained better near-optimal policies than previous API methods such as LSPI and KLSPI.

During the past decade, Rapidly-exploring Random Tree (RRT) and its variants are shown to be powerful sampling based single query path planning approaches for robots in high-dimensional configuration space. However, the performance of such tree-based planners that rely on uniform sampling strategy degrades significantly when narrow passages are contained in the configuration space. Given the assumption that computation resources should be allocated in proportion the geometric complexity of local region, we present a novel single-query Multi-RRTs path planning framework that employs an improved Bridge Test algorithm to identify global important roadmaps in narrow passages. Multiple trees can grown from these sampled roadmaps to explore sub-regions which are difficult to reach. The probability of selecting one particular tree for expansion and connection, which can dynamically updated by on-line learning algorithm based on the historic results of exploration, guides the tree through narrow passage rapidly. Experimental results show that the proposed approach gives substantial improvement in planning efficiency over a wide range of single-query path planning problems.

To realize real-time goal-oriented navigation for a mobile robot in unpredictable environments, a fuzzy reactive system is proposed in this paper. Besides the establishment of the fuzzy logic system, this paper focuses on the the physical meaning of the parameters in the fuzzy system, and proposes a systematic method to automatically suppress redundant fuzzy rules from the rule base. Under the control of the proposed system with automatic redundant fuzzy rule removal, the mobile robot can preferably avoid obstacles autonomously, and generate reasonable trajectories toward the target in various situations. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and experimental studies.

Detecting and monitoring odour around livestock farms are difficult. In this paper, a dynamic neural network based model is proposed to locate odour dispersion around livestock facilities. The proposed dispersion model can dynamically represent complex or non-steady-state meteorological and topographical features in and around livestock farm areas. The proposed approach can also model odour dispersions from multiple odour sources, and from various types of sources such like point source, line source, and area source. In addition, the proposed model simulates the odour dispersion through the dynamic neural activity landscape, without explicitly additional models of the dynamic environment, odour sources, and farming activities.

A robotic system for harvesting tomatoes in greenhouses is designed. Effective recognition of ripen tomatoes from complex background is the key technology of the harvesting robotic system. In this work, the color feature of ripen tomatoes is employed. The ripen tomato is segmented by K-means clustering using the L*a*b* color space. To extract a single integrity ripen tomato, mathematical morphology method is used to denoise and handle the situations of tomato overlapping and shelter. Experimental results show the effectiveness of the proposed method.

Single biometric cryptosystems were developed to obtain win-win scenarios for security and privacy. They are seriously threatened by spoof attacks, in which a forged biometric copy or artificially recreated biometric data of a legitimate user may be used to spoof a system. Meanwhile, feature alignment and quantization greatly degrade the accuracy of single biometric cryptosystems. In this paper, by trying to bind multiple biometrics to cryptography, a cryptosystem named multibiometric cryptosystem (MBC), is demonstrated from the theoretical point of view. First, an MBC with two fusion levels: fusion at the biometric level, and fusion at the cryptographic level, is formally defined. Then four models, namely biometric fusion model,MN-split model, nonsplit model, and package model, adopted at those two levels for fusion are presented. Shannon entropy analysis shows that even if the biometric ciphertexts and some biometric traits are disclosed, the new constructions still can achieve consistently data security and biometric privacy. In addition, the achievable accuracy is analyzed in terms of false acceptance rate/false rejection rate at each model. Finally, a comparison on the relative advantages and disadvantages of the proposed models is discussed.

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

This paper presents a unified treatment of the motion control of underactuated two-link manipulators, including acrobots and pendubots. The motion space is divided into two areas: swing-up and attractive; and control laws are designed for each. First, a control law based on a weak-control Lyapunov function (WCLF) is employed to increase the energy of and control the posture of the actuated link in the swing-up area. Next, one parameter of the WCLF is chosen to be a nonlinear function of the state to avoid singularities. Then, another parameter of the control law is adjusted based on the state to improve the control performance. Finally, an optimal control law is designed for the attractive area. Stability is guaranteed in the swing-up area by the use of a WCLF based on LaSalle's invariance principle. Moreover, the global stability of the control system is guaranteed by integrating the WCLF and a nonsmooth Lyapunov function.

Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both Matlab and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.

The traditional model reference adaptive system (MRAS) is of parallel configuration. By representing the reference model as an equivalent closed-loop subsystem, these two subsystems can share a controller virtually. An embedded structure of MRAS is thereby presented. In this paper, two approaches are developed for designing the adaptive laws. The adaptive law based on Lyapunov stability theory is designed for low-order systems. The adaptive law based on the recursive least-square (RLS) algorithm is suitable for both low- and high-order systems. The RLS-based adaptive law provides a quick convergence rate for the controller adaption. MRAS with RLS-based adaptation law performs better in trajectory tracking and model following. Simulation results show the effectiveness of the proposed method.

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autonomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh et al., 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.

An efficient grid-based distance-propagating dynamic system is proposed for real-time robot path planning in dynamic environments, which incorporates safety margins around obstacles using local penalty functions. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The algorithm is similar to D* but does not maintain a sorted queue of points to update. The resulting gain in computational speed is offset by the need to update all points in turn. Consequently, in situations where many obstacles and targets are moving at substantial distances from the current robot location, this algorithm is more efficient than D*. The properties of the algorithm are demonstrated through a number of simulations. A sufficient condition for capture of a target is provided.

Salmonellais an important infectious disease of livestock and humans. Reported cases that humans caused bySalmonellahave remained high in the world over the past few years. The conventional susceptible-infected-recovery (SIR) models for outbreak analysis of infectious diseases divides the whole population into three groups, a susceptible group, an infected group and a recovery group. In this paper, a new model for the spread ofSalmonellais proposed by introducing the degree of risk for the closed population. Unlike the SIR models that only consider the disease transmission among humans, the proposed model with degree of risk also includes and emphasizes both transmission rates from the environment to humans and person to person. In addition, considering the seasonal effect, an improved model based on degree of risk is proposed, where the transmission rate among humans and the degree of risk can be quantified as a function of temperature. The model perdition of the number of infections from 2000 to 2002 is generally in good agreement with the actual observed datasets. Simulation studies show that the proposed models can deal with the pathogen infections likeSalmonellawith a low transmission rate and a seasonal feature.

The measurement of fundamental frequency is an essential topic for power system control and protection applications. This paper presents a combined approach for accurate and fast tracking of power system frequency on the basis of a zero-crossing algorithm and a three-point algorithm. The accuracy of the classical zero-crossing algorithm is improved by using cubic interpolation with simplified solution. The three- point algorithm is used as an auxiliary method to improve the tracking speed by computing the frequency with only a few sample values. The proposed method is simple, accurate and robust for non-stationary signals and contaminated signals. The performance is demonstrated by simulation results.

A supervised Gauss-Newton (SGN) algorithm for power system frequency estimation is presented in this paper. Taking the signal amplitude, the frequency and the phase angle as unknown parameters, the Gauss-Newton algorithm is applied to estimate the frequency for high accuracy. Meanwhile, a recursive DFT method and a zero-crossing method are used to compute the amplitude and the frequency roughly, so that the parameters can be initialized properly and the updating steps can be supervised for fast convergence of Gauss-Newton iterations. With this combined approach, both high accuracy and good tracking speed can be achieved for power system fundamental frequency estimation.

The eight papers in this special issue focus on networking, sensing, and control for networked control systems (NCS). The papers, which are briefly summarized, examine the architectures, algorithms and applications of NCS.

In this paper, a neurofuzzy-based approach is proposed, which coordinates the sensor information and robot motion together. A fuzzy logic system is designed with two basic behaviors, target seeking and obstacle avoidance. A learning algorithm based on neural network techniques is developed to tune the parameters of membership functions, which smooths the trajectory generated by the fuzzy logic system. Another learning algorithm is developed to suppress redundant rules in the designed rule base. A state memory strategy is proposed for resolving the "dead cycle" problem. Under the control of the proposed model, a mobile robot can adequately sense the environment around, autonomously avoid static and moving obstacles, and generate reasonable trajectories toward the target in various situations without suffering from the "dead cycle" problems. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

This paper presents a novel control strategy based on a non-smooth Lyapunov function to guarantee the stability of the system in the whole motion space. Three control laws that are derived based on three Lyapunov functions are applied successively in three stages of motion control to achieve a suitable posture and increase the energy so as to make the acrobot move into the area around the unstable inverted equilibrium position, and to stabilize it at that position. These three Lyapunov functions are combined into one non-smooth function, which theoretically guarantees the stability of the acrobot in the whole motion space. Simulation results have demonstrated the validity of this strategy

This paper presents a comprehensive unified control strategy for underactuated two-link manipulators, including acrobots and pendubots. The motion space is divided into two areas: swing-up and attractive; and control laws are designed for each. First, a control law based on a weak control Lyapunov function (WCLF) is employed to increase the energy and control the posture of the actuated link in the swing-up area. Next, one of the parameters of the WCLF is chosen to be a nonlinear function of the state so as to avoid any singularities. Then, another parameter in the control law is adjusted based on the state to improve the control performance. Stability is guaranteed in the swing-up area by the use of a WCLF based on the LaSalle invariant theorem. Finally, the global stability of the control system is guaranteed by the use of a non-smooth Lyapunov function integrated with a minimum-switching strategy. Simulation results for a pendubot and an acrobot show this control scheme to be very effective

A control scheme for dynamic tracking of mobile robots is presented, which integrates a velocity controller based on backstepping techniques and a torque controller based on improved RBF neural networks. Because the torque control strategy derived from sliding modes depends on the dynamics of mobile robots, the robustness of the system cannot be guaranteed due to the uncertainties of robot dynamics. In order to decrease the impact of the uncertainties and improve the robustness of the system, improved RBF neural networks are designed online to model the dynamics of mobile robot. Thus the torque controller based on sliding mode is composed of a neural network controller and a robust compensator. Simulations demonstrate the efficacy of the proposed system for robust tracking of mobile robots

A rectangular cell map representation and a neural dynamics based technique are proposed for real-time map building and complete coverage navigation (CCN) of a cleaning robot. The proposed model is compared with a triangular cell map based method proposed by Oh et al. (2004), which combined distance transform path planning, wall-following algorithm, and template based technique. Our method does not need any templates, even in unknown environments. A local map composed of rectangular cells is built through the proposed neural dynamics during CCN with restricted sensory information in unknown environments. The robot is able to dynamically build an accurate map of its immediate limited surroundings for its navigation. Comparison studies to triangular cell map based CCN approach show that the proposed model is capable of planning more reasonable and shorter coverage path in unknown environments

A hybrid navigation approach for mobile robots is presented in this paper, which integrates partial motion planning with emotion-based behavior coordination. According to the local sensing information and assigned goal, the partial motion planning determines a local target point for the mobile robot, and the trajectory composed of the local targets is near optimal in the sense of a given cost function. Behavior coordination based on emotional mechanism is proposed in the navigation control of mobile robots. The emotion system can properly reflect environment states, and then effectively synthesize rational and complex behaviors. The proposed hybrid approach is competent for the mobile robot to navigate autonomously and effectively in unknown environments. Simulation results demonstrate that the robot motion trajectory using the proposed hybrid navigation approach is more reasonable than the one using a pure behavior-based control or emotion-based behavior coordination alone

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.

Area-covering operation is a special kind of path planning, which requires the robot path to cover every part of the workspace. In this paper, a neural dynamics based algorithm is proposed for real-time map building and area-covering operations. A local map composed of squared cells is built through the proposed neural dynamics during area-covering operations with limited sensory information in unknown environments. The robot is able to dynamically build an accurate map of its immediate limited surroundings for its navigation. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley’s membrane equation. The robot can only sense a limited measurable range and the obtained sensory information are used for its navigation. The feasibility of the proposed algorithm is validated by simulation studies on cases under unknown environments.

In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features a unique and simple path representation and a simple but effective evaluation method. The knowledge based genetic algorithm is capable of finding an optimal or near-optimal robot path in both complex static and dynamic environments. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed GA for solving robot path planning problem is demonstrated by a comparison study.

In this paper, a novel biologically inspired approach to real-time tracking control of a nonholonomic mobile robot is proposed. The proposed algorithm incorporates a neural dynamics model derived from a biological membrane equation with the conventional full-state tracking control technique. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow desired trajectories. The proposed approach resolves the speed jump problem existing in some previous tracking controllers. In addition, it can track both continuous and discrete paths. The practicality and effectiveness of the proposed tracking controller were demonstrated by simulation and comparison results.

In this paper, a novel biologically inspired tracking controller is proposed for real-time navigation of nonholonomic mobile robots, which is inspired by the agonist/antagonist effects in muscular sensory motor reaction of a gated dipole neural model. Through the incorporation of the biological computation element, the proposed controller is capable of generating smooth, bounded acceleration command signals for the mobile robot to track reference paths. It resolves the velocity jump problem in the conventional backstepping controllers that result from the initial tracking errors or the discontinuities in discrete paths. In addition, the proposed controller removes the unpractical assumption of "perfect velocity tracking" in some existing approaches. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and comparison studies.

In this paper, a novel fuzzy logic control system is developed for reactive navigation of a behavior-based mobile robot in dynamic environments. A combination of multiple sensors is equipped to sense the obstacles near the robot, the target location and the current robot speed. A fuzzy logic system with 48 fuzzy rules is designed, which consists of three behaviors: target seeking, obstacle avoidance and barrier following. The "symmetric indecision" problem is resolved by several mandatory-turn rules, while the "dead cycle" problem is resolved by a state memory strategy. Under the control of the proposed fuzzy logic model, the mobile robot can preferably "see" the environment around, and avoid static and moving obstacles automatically. The robot can generate reasonable trajectories toward the target in various situations without suffering from the "symmetric indecision" and the "dead cycle" problems. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

A novel tracking control scheme for nonholonomic mobile robots is developed, which integrates two adaptive neurons or non-adaptive neurons, backstepping technique and sliding mode. The proposed control system includes a velocity controller embedded with two biological neurons and a torque controller based on sliding mode. An adaptive neuron model is presented to extend the functions of the neuron model. Two adaptive neurons are embedded into the backstepping-based velocity controller to improve the tracking performance of the proposed control scheme. The proposed systems can completely eliminate the sharp speed jumps existing commonly in the mobile robot systems and is able to ensure the mobile robot to navigate safely under any circumstance. The simulation results demonstrate the effectiveness and efficiency of the proposed control scheme.

This paper develops a new approach of interactive assembly planning. This approach provides an intuitive hand-based interface for human operators to perform assembly operations directly in the virtual world. Its interactive planner then extracts the knowledge of mechanical assembly, and determines motion paths at run time with a biologically inspired neural network. From a single user-defined assembly sequence, this approach is able to automatically produce alternative assembly sequences with robot-level instructions.

In this paper, an embedded fuzzy controller for a nonholonomic mobile robot is developed. The mobile robot was built based on the behavior-based artificial intelligence, where several levels of competences and behaviors are implemented. A class of fuzzy control laws is formulated using the Lyapunov's direct method, which can guarantee the convergence of the steering errors. Theoretical analysis of the fuzzy control algorithms for steering control of the mobile robot is performed. The requirements for a suitable rule base selection in the proposed fuzzy controller are provided, which can guarantee the asymptotical stability of the system. Simulation and experimental studies are conducted to investigate the performance of the proposed fuzzy controller. It can achieve the desired turn angle and make the mobile robot follow the target trajectory satisfactorily.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.

In this paper, a fuzzy controller is developed for of an autonomous nonholonomic mobile robot, which was successfully built with behavior-based artificial intelligence that is implemented by several levels of competences and behaviors. The Lyapunov's direct method is used to formulate a class of control laws that guarantee the convergence of the steering errors to zero. Certain constraints for the control laws are presented for the selection of a suitable rule base for the fuzzy controller, which makes the system asymptotically stable. The stability of the proposed fuzzy controller is proved theoretically and demonstrated by simulation studies. Experiments are also conducted to investigate the performance of the developed fuzzy controller.

For Internet-based tele-robotic systems (Internet robots), the most challenging and distinct difficulties are associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth availability, which might lead to dramatic performance degradation or even instability. In this paper, a new approach to dealing with these problems is explored and implemented. Specifically, a rate-based end-to-end transport protocol is developed for real-time data transmission and an adaptive control scheme is developed to control the robot remotely. A mobile robot teleoperation system, ArtBot-I, is developed to verify and test the solutions. In the experiments, the users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely via the Internet using a web browser.

In this paper, a cooperative sweeping strategy with deadlock avoidance of complete coverage path planning for multiple cleaning robots in a changing and unstructured environment is proposed, using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots. The multiple cleaning robots sweeping will not be trapped in deadlock situations.

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

In this paper, a neural network based torque controller is proposed for real-time collision-free navigation of nonholonomic mobile robots. A torque resulted from the obstacles is incorporated in the control design based on the artificial potential technique, which locally pushes the robot away from the obstacles to avoid collisions. All the needed environment information can be obtained from on-board robot sensors that have limited visibility range only. A torque from a simply single-layer neural network is employed to learn the completely unknown robot dynamics. The system stability is guaranteed by a Lyapunov stability theory. The real-time fine control of mobile robots is achieved through the on-line learning of the neural network. The effectiveness of the proposed controller is demonstrated by simulation studies in both static and dynamic environments.

Mobile robots have been one of the challenging topics of control due to their nonholonomic property and restricted mobility. This paper presents the design of a novel tracking controller for a mobile robot by integrating the neural dynamics model into a conventional feedback controller. The proposed controller is capable of generating real-time smooth velocities, and driving the mobile robot to track desired trajectories. It resolves the speed jump problem existing in some previous tracking controllers. Lyapunov stability theory is used to prove the stability of the control system and the convergence of tracking errors to zero. The effectiveness of the proposed controller is demonstrated by simulation and comparison studies.

In this paper, a novel neural network approach to self-organizing behavior of a multi-robot system is proposed, which is capable of controlling a group of mobile robots to achieve multiple tasks at several different locations, such that the desired number of robots will arrive at every target location from any arbitrary initial robot locations. The proposed model is based on a self-organizing map (SOM) neural network. Unlike some conventional approaches to multi-robot path planning for multiple tasks where the task assignment and path planning are handled separately, this model combines the robot task requirement and motion planning together, such that the robots can start to move once the total tasks are set. The robot navigation can be dynamically adjusted to guarantee each target location will have the desired number of robots, even under unexpected uncertainties, such as one robot breaks down. In addition, unlike the conventional models that are suitable for static environment only, the proposed approach is also capable of dealing with changing environment. The effectiveness of the proposed approach is demonstrated by simulation studies.

Abstract: A software agent is defined as an autonomous software entity that is able to interact with its environment. Such an agent is able to respond to other agents and/or its environment to some degree, and has some sort of control over its internal state and actions. In belief–desire–intention (BDI) theory, an agent's behavior is described in terms of a processing cycle. In this paper, based on BDI theory, the processing cycle is studied with a software feedback mechanism. A software feedback or loop‐back control mechanism can perform functions without direct external intervention. A feedback mechanism can continuously monitor the output of the system under control (the target system), compare the result against preset values (goals of the feedback control) and feed the difference back to adjust the behavior of the target system in a processing cycle. We discuss the modeling and design aspects of an autonomous, adaptive monitoring agent with layered control architecture. The architecture consists of three layers: a scheduling layer, an optimizing layer and a regulating layer. Experimental results show that the monitoring agent developed for an e‐mail server is effective.

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

This paper investigates the introduction of biologically inspired intelligence into virtual assembly. It develops a approach to assist product engineers making assembly-related manufacturing decisions without actually realizing the physical products. This approach extracts the knowledge of mechanical assembly by allowing human operators to perform assembly operations directly in the virtual environment. The incorporation of a biologically inspired neural network into an interactive assembly planner further leads to the improvement of flexible product manufacturing, i.e., automatically producing alternative assembly sequences with robot-level instructions for evaluation and optimization. Complexity analysis and simulation study demonstrate the effectiveness and efficiency of this approach.

Multi-sensor fusion has been a hot topic in the field of robotics. Inspired by the modern philosophy's spirit, the behavior-based systems interact with the real world directly. In this study, a fully autonomous mobile robot is developed that extracts all its knowledge from physical sensors and expresses all its goals and desires as physical action to affect its environment. The control software implements behavior-based artificial intelligence, where the coordination between various sensors are realized by layers of several simple and primitive behaviors similar to those observed in animals. In the developed mobile robot, each module itself generates behaviors. Behaviors corresponding to different sensors have different priorities, where the vision system has the lowest priority, and the ultrasonic sensors and bumper sensors have higher priority. The effectiveness of the developed system is demonstrated by experimental studies.

Event based controller design was first proposed by Xi (1993), where a suitable non-time motion reference is introduced to represent the desired and measurable system output. It has been successfully applied to many areas such as robot motion control, multirobot coordination, force and impact control, robotic teleoperation and manufacturing automation. In this paper, a non-time based tracking controller for multiple nonholonomic mobile robots is proposed by combining the conventional event based control technique with a biologically inspired shunting model first proposed by Grossberg (1982). The proposed control algorithm can generate smooth and continuous velocity control commands. These commands remove the small tracking error constraints in the conventional non-time based controllers. Thus, these controllers keep multiple robots in a required formation and coordinate in the presence of environment change. The effectiveness of the proposed controller is demonstrated by simulation and comparison studies.

Real-time fine motion control of a nonholonomic mobile robot is investigated, where both the robot dynamics and geometric parameters are completely unknown. A neural network controller combining both kinematic control and dynamic control is developed. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that express the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot parameters. The learning algorithm is computationally efficient. The system stability and the convergence of tracking errors to zero are rigorously proved using a Lyapunov stability theory. The real-time fine control of a mobile robot is achieved through the online learning of the neural network. In addition, the developed controller is capable of learning the kinematic parameters online. The effectiveness and efficiency of the proposed controller is demonstrated by simulation studies.

In real world applications there exist arbitrarily shaped obstacles in the workspace during complete coverage path planning of cleaning robots. A cleaning robot should be able to sweep in a variety of corners and in the vicinity of arbitrarily shaped obstacles in an indoor environment. Consequently, the robot is required not only to effectively avoid the obstacles, but also to delicately cover every area in the vicinity of obstacles. In the paper, a solution to vicinity problem of obstacles in complete coverage path planning is proposed using neural-neighborhood analysis. The path planner is a biologically inspired neural network. The proposed model is capable of planning a real-time path to reasonably cover every area in the vicinity of obstacles. The robot path is autonomously generated through the dynamic neural activity landscape of the neural network and the previous robot location. The effectiveness of the proposed approach is verified through computer simulations.

A novel neural dynamics based approach to smooth, continuous and collision-free path generation of an autonomous nonholonomic mobile robot is proposed. The robot behavior, such as target acquisition and obstacle avoidance, are completely controlled by two control variables, the heading direction and the forward velocity of the robot. The dynamics of these control variables is characterized by a biologically inspired shunting neural model, whose inputs are from the target and obstacles that are acquired relying on measurable sensors information only. The target input produces an attractive force, while the obstacle inputs form repulsive forces to the mobile robot. Each force votes for a certain value of control variables that have unique values at a certain time. The collision-free path and the velocity control commands of the robot are generated through the dynamics of control variables. The kinematic constraints of mobile robot is respected. A series of simulation results show that the proposed approach can be successfully applied to both static and dynamic environments, as well as multi-robot systems with effective and efficient computation.

An area-covering operation is a kind of complete coverage path planning, which requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum, robots, painter robots, land mine detectors, lawn mowers, and windows cleaners. In this paper, a novel biologically inspired neural network approach is proposed for complete coverage path planning with obstacle avoidance of a cleaning robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The proposed model algorithm is computationally efficient, and can also deal with changing environment. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot path.

A vision-based landmark recognition system by using the evolutionary principle for robot navigation tasks is implemented in this study. The research is aimed at using the GA to do pattern matching. The basic idea is to use genetic algorithms to find the best matching between nodes of the two patterns. The evaluation function can be defined in terms of total differences in magnitudes of nodes between the desired pattern and the real pattern. A search method based on genetic algorithms for pattern recognition in digital images is implemented as the vision layer for a behavior based mobile robot. The vision layer can recognize artificial landmarks by searching all the pro-defined patterns using the GA. Then it generates the desired behavior corresponding to various landmarks. The results of the algorithm is promising and has a high accuracy in classifying the input patterns. The effectiveness of the developed system is demonstrated by simulation and experimental studies.

A biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a nonstationary environment. The real-time robot trajectory with obstacle avoidance is rated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation. The varying environment is represented by the dynamic activity landscape of the neural network. Where the neural activity propagation is subject to the kinematic constraint of the nonholonomic mobile robots. The real-time tracking velocities are generated by a novel neural dynamics based controller, which is based on two shunting models and the backstepping technique. Unlike the backstepping controllers that produce non-smooth velocity commands with sharp jumps, the proposed tracking controller is capable of generating smooth, continuous commands not suffering from velocity jumps. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.

In this paper, a novel tracking control approach is proposed for real-time navigation of a nonholonomic mobile robot. The proposed tracking controller is based on the error dynamics analysis of the mobile robot and a neural dynamics model derived from Hodgkin-Huxley's membrane model of a biological system. The stability of the control system and the convergence of tracking errors to zeros are guaranteed by a Lyapunov stability theory. Unlike many tracking control methods for mobile robots where the generated control velocities start with large initial velocities, the proposed neural dynamics based approach is capable of generating smooth, continuous robot control signals with zero initial velocities. In addition, it can deal with the situation with a very large tracking error. The effectiveness and efficiency are demonstrated by comparison and simulation studies.

In this paper, shunting neural networks are proposed for dynamic planning and control of robots. The dynamic environment is represented by a neural activity landscape of a neural network, where each neuron in the topologically organized neural network is characterized by a shunting equation that is derived from Hodgkin and Huxley's (1952) biological membrane equation. The collision-free path is generated in real-time from the activity landscape without any explicit searching procedures and without any prior knowledge of the dynamic environment. The real-time tracking control of robots to follow the planned dynamic path is designed using shunting equation as well. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach with encouraging experimental results.

This paper presents a novel approach toward the real-time rendering of artificial guidance in virtual programming. It models the operational characteristics of robot programming with a biologically inspired neural network. Through neural activity propagation, this approach creates a new means of assisting the direct manipulation of virtual objects with suggested moving paths. In addition, it introduces a tracking controller to ensure smooth moving velocities. Finally, simulation results are provided to demonstrate the effectiveness and efficiency of the proposed approach.

An artificial neural network that considers the system as a black box is designed for the mass transfer modeling of supercritical fluid extraction. The proposed neural network assumes a three-layer structure with a fast backpropagation learning algorithm. In addition, a hybrid model using both a neural network and the Peng-Robinson state equation is developed for supercritical fluid extraction, where the neural network is used to generate the non-linear binary interaction parameter of the Peng-Robinson state equation. Various temperatures, pressures, and solubility in literature are used to train the proposed models. The predictions of the proposed neural network models are compared to a conventional model with a Peng-Robinson equation of state in literature. Generally the results using the proposed models are better than those using the conventional model. The effectiveness of the proposed neural network approaches are demonstrated by simulation and comparison studies.

In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

In this paper, a novel biologically inspired neural network approach is proposed for real-time motion planning with obstacle avoidance of a nonholonomic car-like robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. The robot configuration space constitutes the state space of the neural network. There are only local connections among neurons. Thus the computational complexity linearly depends on the neural network size. The neural activity propagation is subject to the kinematic constraints of the nonholonomic car-like robot. The real-time robot motion is planned through the dynamic neural activity landscape without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore the model algorithm is computationally efficient. The stability of the neural network system is proved by qualitative analysis and a Lyapunov stability theory. Simulation in several computer-synthesized virtual environments further demonstrates the advantages of the proposed approach with encouraging experimental results.

This study was undertaken to determine the relationship between low-back pain and spinal motion. Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. Each of the subjects performed a standard battery of spinal motions including bending in all planes. At the completion of each desired motion, each subject was asked to self-report the pain they experienced on a 10 point scale. The 3D location of markers attached to the pedicle screws was recorded for each motion. Intra- and inter-vertebral motions were calculated. The time series data was reduced to the ranges of motion for each of the tests. A three-layer neural network with fast back-propagation learning algorithm was designed to investigate the relationship between the pain and the motion parameters. This model provided an accurate model for prediction of low-back pain from segmental spinal motion and also offered insights into the mechanisms causing mechanical low-back pain.

Foodborne infections are a significant cause of morbidity and mortality in human populations. Risk assessment and public health control measures could be greatly enhanced by establishing an accurate relationship between ingested dose and infection, and defining minimum infectious doses. In this paper, a novel neural network model is proposed for the dose response of foodborne pathogens. The proposed model assumes a three-layer structure with a fast backpropagation learning algorithm. The model predictions for four available data sets from the literature are compared using six statistical models (log-normal, log-logistic, simple exponential, flexible exponential, /spl beta/-Poisson and Weibull-Gamma). The methods of least square error, maximum likelihood and correlation coefficient are used for the comparison, and they show that the neural network model does better than the statistical models. Predictions of dose response for multiple types of pathogens and with different host age and gender using neural network models are discussed, with simulations.

Virtual assembly computer tools assist human operators in their work of mechanical assembly inside virtual environments. This paper investigates the real-time rendering of computer-synthesized guidance. It uses a biologically inspired neural network to generate collision-free paths, and helps users to specify assembly tasks by guiding the motion of objects. This work leverages the power of "manufacturing in the computer" with the creation of artificial guidance that could be unavailable in the physical world.

Working in the virtual environment has the advantage of unsurpassed support from computer tools. However, its success in industrial applications still relies on how accurately a virtual environment represents the real-life activities. Presented in this paper is a method of reactive assembly planning that is able to handle environmental uncertainty. It uses automatic object modeling to update the virtual environment according to the changes in a working environment, and a biologically-inspired neural network to reactively generate collision-free paths in real time.