This work evaluates the effectiveness of a novel one-way time of flight-based passive ultrawideband (UWB) inertial navigation solution for indoor positioning of a mobile platform and formulates a design criterion for choosing between passive systems through noise analysis. The proposed tightly coupled time of arrival (TC-TOA) approach achieves passive synchronization by keeping track of the local clock and its derivatives as states. Using these states and position estimate, the reception timestamps of the network messages are predicted. The errors in these predicted timestamps are then used to update both clock states and the position states through accurate modeling of coupled interactions. The design results in a tightly coupled estimator formulation achieved using an error state Kalman filter with right quaternion error parameterization. The proposed method is evaluated using a MATLAB simulation environment and on a dataset acquired by flying a quadcopter in an indoor environment, with ground truth obtained from a motion capture system. Decawave DWM 1000-DEV hardware with custom firmware was used in the measurement acquisition process. Simulation results demonstrate that the proposed tightly coupled system outperforms time difference of arrival (TDOA)-based methods, especially when the network transmission gap becomes large or in the presence of communication interruptions, which can happen in large-scale networks. Experimental validation resulted in RMS-position errors around 25 cm, with increasing differences between the TC-TOA versus TDOA methods as the number of anchors drops. Furthermore, the proposed method can accommodate measurement updates even when the connection with the network is interrupted down to one anchor.

This paper introduces an artificial intelligence-based landing zone detection module (LZDM) for vertical take-off and landing (VTOL) navigation. It employs a projection-based point cloud semantic segmentation (PCSS) convolutional neural network model combined with point cloud accumulation and a range image generation module. The proposed method addresses the limitations of existing projection-based PCSS methods, which often struggle with low-resolution and non-repetitive scan raw light detection and ranging (LiDAR) data commonly found in aerial datasets. The proposed LZDM was developed using three sets of aerial datasets collected from a DJI M600 hexacopter drone, a DJI M300 RTK quadrotor, and a Bell412 helicopter. The results were evaluated using both qualitative and quantitative metrics, demonstrating its robustness and effectiveness. In terms of quantitative results, the proposed method achieved mean intersection over union and accuracy values greater than 0.93 and 98 percent, respectively, across all three datasets, highlighting its accuracy in identifying safe landing zones (LZs). To assess the real-time feasibility of the proposed LZDM, it was deployed on a reconfigurable hardware-accelerated module. This setup achieved processing rates higher than 10 Hz for all three datasets and a throughput of over 5 million pts/s on the Jetson AGX Xavier dedicated hardware combined with the PyTorch TensorRT optimization module. The supplementary materials, including the inference code, sample testing data, and instructions are available in our GitHub repository at https://github.com/nira16herath/CENet-LZ-detection/tree/main.

This paper introduces a novel method utilizing generative adversarial networks (GANs) for enhancement and semantic segmentation of radar images for autonomous navigation applications. Radar sensors are known for their robustness in adverse weather conditions compared to other perception sensors such as LiDAR and cameras. However, their application in autonomous vehicles (AVs) is often limited due to the low-resolution data they produce. The primary aim of this study is to enhance radar images captured by AVs, enabling these vehicles to rely on radar sensors for object identification and semantic segmentation in all weather conditions. The training of the GAN was performed using ground truth images derived from high-resolution LiDAR point cloud maps and radar images collected in good weather conditions. These ground truth images were generated through a customized LiDAR scan accumulation method, followed by a two-dimensional (2D) projection and cropping process. Additionally, a customized data augmentation method was employed during the training process to improve the performance of the proposed method in adverse weather conditions. During the inference phase, our approach exclusively uses radar images to produce enhanced and semantically segmented versions of the input radar images. The effectiveness of the proposed method is validated through both qualitative and quantitative results, demonstrating its capability to generate enhanced and semantically segmented images from radar images in all weather conditions. The supplementary materials, including the inference code, sample testing data and GAN models, are available in our GitHub repository.https://github.com/thaki94/riess-gan

This study investigates the effectiveness of a passive tightly coupled ultra-wideband (UWB) based inertial navigation system for indoor positioning of mobile platforms. Unlike conventional methods that rely on time difference of arrival (TDOA) or two-way ranging (TWR) measurements, the proposed approach utilizes local reception timestamps directly. An error state Kalman filter with right quaternion error definition is used in the state estimation process. Evaluation is performed first in a Matlab simulation environment and then, using a dataset acquired by flying a quadcopter while monitored by a motion capture system. Timestamp measurements were acquired using custom firmware flashed onto Decawave DWM 1000-DEV hardware. Our findings demonstrate that the proposed system outperforms traditional TDOA methods, providing accurate measurements even in the presence of communication interruptions, with as few as one anchor.

This paper presents a comprehensive review of state-of-the-art navigation methods available for unmanned aerial vehicles (UAVs) used in parcel delivery. Particularly, the paper focuses on state-of-the-art sensor configurations, multi-sensor data fusion architectures, and their performance when employed for UAV navigation. Additionally, this paper presents the associated safety regulations for UAV navigation currently imposed by regulatory bodies in US and Canada. The existing navigation solutions sometimes produce degenerative results due to GPS loss, multipath signals, spoofing events, and other sensor degradation scenarios. Therefore, this article investigates the suitability of integrating visual lidar odometry and mapping (VLOAM) with GPS to overcome the limitations of existing navigation solutions. A comparative study of the multi-sensory combined solutions is presented with numerical simulations, validating the regulatory compliance of VLOAM and GPS integrated system under common GPS failure cases. Note to Practitioners—This work was motivated by the need for a survey on existing UAV navigation methods for parcel delivery applications. Different UAV navigation methods exist, depending on the sensors used and the sensor fusion architectures, with varying degrees of localization accuracy. It can be challenging for researchers and practitioners to decide which method to adopt for their application while complying with the existing safety regulations. Therefore, this paper presents an overview of the current safety regulation for UAV navigation and evaluates the state-of-the-art navigation methods against regulatory safety compliance. Additionally, a numerically validated safe navigation method is suggested for UAV-based parcel delivery. This paper provides researchers and practitioners with comprehensive reference sources in the UAV navigation field, which can help them develop suitable solutions to ensure safe navigation.

This paper presents the design of an Interacting Multiple Model (IMM) filter for improved navigation performance of Micro Aerial Vehicles (MAVs). The paper considers a navigation system that incorporates rotor drag dynamics and proposes a strategy to overcome the sensitivity of the system to external wind disturbances. Two error state Kalman filters are incorporated in an IMM filtering framework. The first filter has a model that uses conventional Inertial Navigation System (INS) mechanization equations, while the second filter considers a dynamic model with rotor drag forces of the MAV. In order to support the two error state Kalman filters, the generic IMM algorithm [1] is modified for error state implementation, handle dissimilar state definitions, and adaptive switching during operation. Numerical simulations and experimental validation using the EuRoC dataset are conducted to evaluate the performance of the proposed IMM filter design for changing flight conditions and external wind disturbance scenarios.

This paper presents the development of a Kalman filter-based range estimation technique to precisely calculate the inter-node ranges of Ultra Wide Band (UWB) modules. Relative clock tracking filters running between every anchor pair tracks relative clock dynamics while estimating the time of flight as a filter state. Both inbound and outbound message timestamps are used to update the filter to make the time of flight observable in the chosen state space design. A faster relative clock filter convergence has been achieved with the inclusion of the clock offset ratio as a measurement additional to the timestamps. Furthermore, a modified gradient clock synchronization algorithm is used to achieve global clock synchronization throughout the network. A correction term is used in the gradient clock synchronization algorithm to enforce the global clock rate to converge at the average of individual clock rates while achieving asymptotic stability in clock rate error state. Experiments are conducted to evaluate synchronization and ranging accuracy of the proposed range estimation approach.

The low oil price environment is driving the oil and gas (O&G) industry to become more innovative and deploy smart field technologies, to increase operational and asset efficiency, minimize health, safety, and environmental (HSE) risks, improve asset portfolio, reduce capital and operation costs, and maximize capital productivity. The Internet of Things (IoT) is at the forefront of this digital transformation, enabling seamless real-time data collection, processing, and analysis from a range of equipment, processes, and operations to achieve these objectives. There are various operations/applications in the upstream, midstream, and downstream sectors (e.g., condition-based monitoring and location tracking) for which IoT-enabled solutions have a significant impact and offer a range of opportunities to increase socioeconomic benefits. However, there are several impediments (e.g., vulnerability to cyber attacks, lower technological readiness for deploying in zone-0 and zone-1 hazardous environments, unavailability of communication infrastructure, labor concerns, and maintenance and obsolescence) that slow the pace of adoption of IoT technologies for regular upstream, midstream, and downstream operations. This review article provides an overview and assessment of the role, impact, opportunities, challenges, and current status of IoT deployment in the O&G industry.

This paper presents a prototype of an automated seedling height assessment system for tree nurseries. The proposed system can acquire and store real-time 3D point-cloud data of seedlings; and perform offline identification, measurement, and report generation of seedling heights with an overall system accuracy that meets a 5mm accuracy specification. Periodic growth information of seedlings allows quantifying effects of different factors on the overall seedling development process for research and production optimization purposes. However, current manual sampling approaches used at these facilities produce quite limited data samples, and the process is rather time-consuming and labor intensive for industrial scale operations. In contrast, the proposed system is capable of significantly increasing the measurement sample size, measurement resolution, and frequency of measurement by automating the seedling measurement process using a scanning laser profilometer and an application specific point-cloud processing algorithm. The performance of the proposed profilometry solution for point-cloud generation is compared with several other point-cloud generation methods such as a 3D structured light sensing, light intensity detection and ranging (LiDAR), stereovision, and photogrammetry. This comparison results demonstrate a superior performance of the laser-profilometer over other sensing solutions available for seedling height measurement. The proposed system is experimentally validated for its measurement accuracy and repeatability. The field-test of the measurement system was conducted at Centre for Agriculture and Forestry Development, Wooddale, Newfoundland and Labrador (NL), Canada, and the results demonstrate the practical applicability and technological readiness of the proposed system for field deployment.

This study performs a nonlinear observability analysis on range assisted inertial navigation system (INS) for quadrotor micro-aerial vehicles (MAV). The INS is formulated incorporating the quadrotor dynamics with aerodynamic drag forces. The observability analysis is carried out for cases where three and two range measurements are available. The analysis facilitates the range assisted localization of MAVs when there are less than four range measurements are available. The primary objective of this study is to identify the conditions under which the INS becomes unobservable, and these conditions are validated through numerical simulation. The main contributions of this paper are as follows, 1. Nonlinear observability analysis of the range assisted INS for quadrotor MAVs. 2. Theoretical derivation and numerical validation of unobservable conditions for three and two range cases. 3. Experimental validations of estimator performance.

In this paper, we introduce a novel approach of reducing the communication load in distributed model predictive control (DMPC) for mobile robots. The key idea is to project the predicted state trajectory onto a grid resulting in an occupancy grid prediction. This approach has the advantage of utilizing continuous optimization methods while only quantized information is exchanged. We consider non-holonomic mobile robots to numerically and experimentally investigate the proposed method.

In this paper, we present an advanced real-time Visual Inertial Navigation System (VINS) based on Multi-State Constraint Kalman Filter (MSCKF). This filter uses Cubature Kalman Filter (CKF) for nonlinear measurement update and Maximum Likelihood Estimate (MLE) to optimize the estimate, which in turn provides better system accuracy and stability. The measurement model is developed basing Trifocal Tensor Geometry (TTG), which allows replacing the 3D feature-point reconstruction step as in traditional VINS systems. Alternatively the available Unscented MSCKF [1] based on Unscented Kalman Filter has an implementation issue of executing the square-root operation of the covariance matrix due to the negatively-weighted sigma points, and this may halt the filter operation or even causes the system to fail. The proposed CKF structure has the ability to carry the highly-nonlinear TTG-based measurement model as well as overcome the issue associated with the covariance square-root operation. The MLE based iteration is applied to optimize the visual measurement update where it performs multiple corrections on a single measurement. This procedure helps to minimize the error accumulation allowing the filter to operate for longer durations. The proposed Iterated Cubature MSCKF is tested using KITTI datasets [2] and compared against the Unscented MSCKF and non-iterated Cubature MSCKF.

The main reported solutions for the problem of multi-robot relative localization require synchronous communication between robots, where the network should communicate each time a relative measurement is logged in the team. This paper proposes a localization method, which can accommodate communication at a low predefined rate rather than forcing communication each time a measurement is logged. This is achieved without explicitly accumulating past measurements locally at each robot. This capability is necessary to support increasing number of robots in a team, under finite communication and computation resources. The design includes a novel fusion strategy, a consistent estimation method, and a state based initialization method, embedded in a distributed target tracking framework. The design is efficient in terms of computation demand, since it scales linearly with the number of robots. Additionally, the design is efficient in terms of communication demand, since communication is neither required to be synchronized with sensor readings, nor constrained to a specific network topology. The paper validates the proposed approach for its initialization capability, consistency of estimates, and robustness of performance, through several numerical simulations and using a publicly available multi-robot data set.

This paper presents a distributed leader-assistive localization approach for a heterogeneous multirobotic system (MRS). The localization algorithm is formulated to estimate the position and orientation (pose) of a group of robots in a given reference coordinate frame (or global coordinate frame). It is assumed that the heterogeneous-MRS has one or a group of robots (which we refer as leader robots) with higher sensor payload, higher processing power, and larger memory capacity, enabling accurate self-localization capabilities. Robots with limited resources (which we refer as child robots) rely on leader robots, and inter-robot observations between leaders and themselves for localization. Finite-range sensing is a key challenge for such leader-assistive localization. This study presents a sensor sharing technique which virtually enhances the sensing range of leader robots. In the proposed method, each robot locally runs a cubature Kalman filter to estimate its own pose and hosts a low cost, lightweight, and low-power sensory system to periodically measure relative pose of neighbors. Each robot transmits these relative pose measurements to leader robots. Leader robots then combine available relative observations in order to synthesise global pose measurements and associated noise covariances for child robots. Child robots are acknowledged by the leader robots with the synthesized global pose measurements and fuse these measurements with their local belief in order to improve their localization. Theoretical developments are presented to virtually enhance the leader robots' sensing range. The performance of the proposed distributed leader-assistive localization algorithm is evaluated on a multirobot simulation test-bed and on a publicly available multirobot localization and mapping data-set. The results illustrate that the proposed algorithm is capable of establishing accurate and consistent localization for the child robots even when they operate beyond the sensing range of the leader robots. Note to Practitioners-MRS can be used to perform environmental monitoring, exploration tasks, and search and rescue missions. Accurate localization is a critical factor that governs the success of the autonomous mobile robots-based missions. In order to improve the localization accuracy, robots can be equipped with advanced sensory systems which will increase the cost. Additionally, robots can execute advanced localization algorithms to generate an accurate localization which entails higher processing capabilities and higher memory capacity. Most of the robotic systems do not possess sufficient resources to host advanced sensory systems and execute advanced localization algorithms. In a heterogeneousMRS, robots with more accurate localization capabilities (leader robots) can assist robot with limited resources (child robots) for localization. Available leader-assistive localization approaches demand child robots to operate within the sensing range of the leader robots. This constraint limits the teammates' maneuverability, reduces the area covered by the robots, and demands a complex algorithm to avoid collisions among teammates. We address this limitation and propose a novel leader-assistive localization framework. The proposed framework is capable of establishing an accurate and consistent pose estimation for child robots even when they operate beyond the sensing range of leader robots.

Bottom-up visual attention is an automatic behavior to guide visual perception to a conspicuous object in a scene. This paper develops a new object-based bottom-up attention (OBA) model for robots. This model includes four modules: Extraction of preattentive features, preattentive segmentation, estimation of space-based saliency, and estimation of proto-object-based saliency. In terms of computation, preattentive segmentation serves as a bridge to connect the space-based saliency and object-based saliency. This paper therefore proposes a preattentive segmentation algorithm, which is able to self-determine the number of proto-objects, has low computational cost, and is robust in a variety of conditions such as noise and spatial transformations. Experimental results have shown that the proposed OBA model outperforms space-based attention model and other object-based attention methods in terms of accuracy of attentional selection, consistency under a series of noise settings and object completion.

In this paper we demonstrate a design and experimentation of a relative localization solution for a multi robot team involving both ground and aerial robots. The relative localization method proposed in this paper has the ability to localize a dynamic agent with respect to only one leader ground robot in a GPS denied environment. The sensor solution proposed in the study employs a combination of an acoustic sensor and an infra-red(IR) based vision sensor for relative range and bearing estimations respectively. An extended Kalman filter performs the sensor fusion using a four degree of freedom kinematic model. Numerical simulations validate the sensor fusion scheme for both ground and aerial robotic relative localization. An experimental test-bed of the system with the hardware implementation of the sensors were developed. For comparison purposes the self localization modules of the robots are further integrated into the experimental setup. Realtime experiments were performed where 5-10 cm mean accuracy of pose estimation was achieved in multiple experiments.

This paper establishes modular and hierarchical supervisory control theories of Fuzzy Discrete-Event Systems (FDES). It aims to resolve the horizontal and vertical complexities present in large-scale event-driven systems, which are affected by uncertainties in their event and state representations. The modular supervisory control architecture composed of a set of noncommunicating local supervisors, in which one supervisor is assigned for each module having its own sensing and acting capabilities. The notion of separability for languages in FDES is introduced and the property of a language specification of FDES, termed as separably-controllable-observability, is proposed to determine the existence of modular supervisors to the control problem. The hierarchical supervisory control architecture consists of multilevel supervisors assigned to detailed low-level and abstract high-level models of the plant. The notion of output-control-consistency is introduced for languages in FDES. Then, the property called strictly-output-control-consistency is defined for FDES in order to maintain the hierarchical consistency between low-level and high-level FDES modules. The property of H-fuzzy observability is introduced to ensure the hierarchical consistency under the partial observation of low-level FDES. Finally, using the established hierarchical supervisory control theory of FDES, a behavior-based mobile robot navigation example is discussed.

The main objective of this paper is to establish a general architecture for decentralized supervision of fuzzy discrete event systems (FDES). First, two different types of decentralized supervisory control architectures of FDES are presented, which fuse the locally enabled degrees of fuzzy events using the fuzzy-intersection operator and the fuzzy-union operator, respectively. Both of these architectures possess limitations in information association. Second, to overcome the aforementioned drawbacks, a general architecture for decentralized supervisory control of FDES is introduced, in which the decisions of local supervisors are fused by using both fuzzy-union and fuzzy-intersection operators. The proposed general architecture is then implemented to control a tightly coupled multirobot object manipulation task in simulation. A performance evaluation is performed to quantitatively estimate the validity of the proposed architecture compared with centralized FDES-based and decentralized crisp DES-based approaches.

In this paper, we address the multi robot coordination problem of tightly-coupled task execution, using a formal decentralized supervisory control approach. A general architecture for decentralized supervisory control of Fuzzy Discrete Event Systems (FDES), which is capable of modeling asynchronous event driven systems with inherited uncertainties, is developed. This architecture is then incorporated for con trolling behavior-based mobile robots moving in unstructured environments while maintaining a fixed distance between each other, which resembles a tightly-coupled multi robot object manipulation task. The proposed approach is then successfully implemented in simulation with two mobile robots and a performance evaluation is also performed to investigate the validity of the proposed approach over the centralized and crisp Discrete Event System (DES) based approaches.

In order to incorporate the uncertainty and impreciseness present in real-world event-driven asynchronous systems, fuzzy discrete event systems (DESs) (FDESs) have been proposed as an extension to crisp DESs. In this paper, first, we propose an extension to the supervisory control theory of FDES by redefining fuzzy controllable and uncontrollable events. The proposed supervisor is capable of enabling feasible uncontrollable and controllable events with different possibilities. Then, the extended supervisory control framework of FDES is employed to model and control several navigational tasks of a mobile robot using the behavior-based approach. The robot has limited sensory capabilities, and the navigations have been performed in several unmodeled environments. The reactive and deliberative behaviors of the mobile robotic system are weighted through fuzzy uncontrollable and controllable events, respectively. By employing the proposed supervisory controller, a command-fusion-type behavior coordination is achieved. The observability of fuzzy events is incorporated to represent the sensory imprecision. As a systematic analysis of the system, a fuzzy-state-based controllability measure is introduced. The approach is implemented in both simulation and real time. A performance evaluation is performed to quantitatively estimate the validity of the proposed approach over its counterparts.

Visual tracking is a quite challenging issue for a moving robot due to the appearance changes of both the background and targets, large variation of motion, partial or full occlusion and so on. However, humans are capable to cope with those difficulties to achieve satisfactory tracking performance. Thus this paper presents a biologically-inspired method of visual tracking for moving robots by using object-based visual attention mechanism. This tracking method consists of four modules: pre-attentive segmentation, top-down attentional biasing, post-attentive completion processing and online learning of the target model. Experimental results in natural and cluttered scenes are shown to validate this general and robust tracking method.

Visual attention is one of the major requirements for a robot to serve as a cognitive companion for human. The robotic visual attention is mostly concerned with overt attention which accompanies head and eye movements of a robot. In this case, each movement of the camera head triggers a number of events, namely transformation of the camera and the image coordinate systems, change of content of the visual field, and partial appearance of the stimuli. All of these events contribute to the reduction in probability of meaningful identification of the next focus of attention. These events are specific to overt attention with head movement and, therefore, their effects are not addressed in the classical models of covert visual attention. This paper proposes a Bayesian model as a robot-centric solution for the overt visual attention problem. The proposed model, while taking inspiration from the primates visual attention mechanism, guides a robot to direct its camera toward behaviorally relevant and/or visually demanding stimuli. A particle filter implementation of this model addresses the challenges involved in overt attention with head movement. Experimental results demonstrate the performance of the proposed model.

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

This paper describes a leader-follower based formation control framework to coordinate multiple nonholonomic mobile robots. The proposed strategy deploys a control theoretic bottom-up approach where, continuous controllers are coordinated by a supervisory controlled discrete event system. All the mobile robots are required to navigate in an obstacle populated environment. And the followers keep a predetermined geometric formation with the leader while being adaptable to the constraints imposed by obstacles on the environment. The low level control is achieved by a set of behavior based controller with a high-level discrete event system that manages the dynamic interaction with the external environment. The use of discrete event systems reflects a modular manageable system with the potential for scalability and reusability. The proposed system is implemented through simulation and the results are shown to verify its operation.

This paper presents a novel approach to behavior based control of mobile robots using supervisory control of Fuzzy Discrete Event Systems (FDES). Fuzzy events are triggered by the sensor readings and the inference occurs through a fuzzy rule base system. The supervisor can activate and control fuzzy controllable events simultaneously with fuzzy uncontrollable events to achieve the planned objectives. The fuzzy observability concept is incorporated to represent sensor uncertainties. Fuzzy state based controllability and observability measures are also discussed. The proposed theoretical development is then extended to discuss an application with behavior based control of mobile robots.

The interaction between a robot and a human becomes meaningful when the robotic agent possesses some level of human-like cognition. This paper proposes an attention-based approach for multi-modal HRI. The core of the proposed approach is a bio-inspired artificial model of visual attention which enables a robot to focus on the visually salient and/or behaviorally relevant stimuli in the surrounding. The attention model provides the human partner with the opportunity to manipulate the attention behavior of the robot through natural speech command. Similarly the robot is able to manipulate the attention of the human partner using its actuators. Thus the bio-inspired visual attention mechanism along with the sensors and actuators enables the robot to establish joint attention with the human partner. Formation of this joint attention is the basis for further human-robot interaction. Experimental results validate different aspects of the proposed attention-based HRI framework.

This paper describes a landmark position measurement system using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, chairs. When such features are unavailable the processes that depend on landmarks such as navigation and simultaneous localization and mapping (SLAM) algorithms will fail. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser- camera calibration information. Thus, the proposed method exploits the beneficial aspects of each sensor to overcome the disadvantages of the other sensor. Experimental results of an application in SLAM is presented to verify the results.

In this paper an object-based visual attention model extending Duncan’s integrated competition hypothesis is presented for robots. Based on Gestalt rules the model segments the visual field into primitive groupings by evaluating both edge continuity and color similarity. An object representation is also built in long-term memory by using contour and color features. Dependent on the task and object representation, top-down modulation performs on pre-attentive features, followed by bottom-up competition. The object-based salience is evaluated by combination of pixel-wise salience within each pre-attentive grouping. The attended object is finally refined to reach an accurate representation in working memory. This model has been applied into two tasks of mobile robots: task-specific still and moving object detection. Experimental results in cluttered scenes are shown to validate this model.

This paper argues that the object- and space-based modes of visual attention can be naturally integrated in a common mathematical framework. In an earlier work we have proposed a mathematical model of visual attention for robotic system exploiting the knowledge of visual attention mechanism of the primates. This paper investigates on the validity of the proposed model for robotic systems through experimentation on a real robot. The paper sheds light on a number of real world issues involved with the design of visual attention system for physically embodied robots and explains how the proposed Bayesian model of visual attention addresses these issues. The object- and space-based modes of visual attention are naturally integrated in the model and is reflected in the sequential Monte Carlo implementation of the model on a real robot.

This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.

This paper presents a real world application of fuzzy logic and Genetic algorithm (GA) in mobile robotics. It proposes a novel method of integrating fuzzy logic and GA to solve the Simultaneous Localization And Mapping (SLAM) problem of mobile robots. The proposed algorithm, termed as Fuzzy-Evolutionary SLAM, solves the global optimization problem of SLAM where the objective function measures the quality of a robot's pose in accommodating a local map into a partially developed global map of the environment. The search for the optimal robot's pose is performed by a GA. Knowledge on the problem domain is preprocessed by a fuzzy logic system and allows the GA to evolve within a specified region of the search space. It helps to speed-up the GA based search. The proposed algorithm processes data in an incremental fashion and follows essentially no assumption about the environment. Experimental results validate the performance of the proposed algorithm.

This paper proposes a fuzzy PI tuning scheme for multivariable process system where the process consists interactions among loops. First, a static decoupler is implemented and each loop is assigned with a fuzzy PI controller. Two types of FPI configurations are considered. FPI tuning is formulated using two-level tuning principle shown in [1]. The low-level tuning is accomplished using a novel linear tuning scheme, where as high-level tuning is achieved while changing rule base parameters in the fuzzy output. A nonlinearity tuning diagram is developed for nonlinear tuning. The proposed design method is less complex and can be applied for any n x n MIMO system. The performance of the proposed controller is evaluated using control simulations.

This paper presents a novel behavior-based approach for mobile robot control using Fuzzy Discrete Event System (FDES). The method exploits the multi-valued feature of Fuzzy Logic (FL) and event-driven property of Discrete Event System (DES) to define activity of each behavior using fuzzy state vectors. State-based prediction of activity is accomplished using fuzzily defined event matrices. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensor uncertainty and environment dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. The present work employs observability and controllability to modify the velocity commands and the sampling frequency depending on decision vagueness and undesirable state-reach. Real-time results of FDES-based mobile robot navigation are presented to validate the performance of the proposed method.

Identification of moving objects in the vicinity of a mobile robot is important for safe navigation. This paper presents a robust technique for detecting moving objects using a laser ranger mounted on a mobile robot. The proposed method uses two consecutive laser range scans to detect the moving objects in the environment. After the initial alignment of the two laser scans, each laser reading is segmented and classified according to object type, stationary, non-stationary or indeterminate. Laser reading segments are then analyzed using an algorithm to maximally recover the moving objects. The proposed algorithm has the ability to recover all possible laser readings that belong to moving objects. The developed algorithm is verified using experimental results.

This paper presents a novel algorithm for simultaneous localization and mapping (SLAM) of mobile robots. The proposed algorithm, termed as evolutionary SLAM, is based on an island model genetic algorithm (IGA). The IGA searches for the most probable map(s) such that the underlying robot's pose(s) provide a robot with the best localization information. The correspondence problem in SLAM is solved by exploiting the property of natural selection, to support only better performing individuals to survive. The algorithm does not follow any explicit heuristics for loop closing, rather maintains multiple hypotheses to solve the loop closing problem. The algorithm processes sensor data incrementally and therefore, has the capability to work online. Experimental results in different indoor environments validate the robustness of the proposed algorithm

This paper describes a novel behavior-based approach for an object-pulling robot using Fuzzy Discrete Event System (FDES). The object-pulling task is a variant of objectpushing operation, where the robot anchors itself with an object and then navigates to a target location. The proposed behavior-based approach incorporates global and local motion planning for navigation. The global motion planning uses strategic behaviors that employ prior knowledge of the environment to generate a safe-path from the initial position of the robot to the end-point (goal), whereas the local motion planning uses reactive behaviors that deploy locally sensed sensory data for target-object detection and dynamic obstacle avoidance. The major contribution of this paper is the novel FDES-based behavior selection (or modulation) method that eliminates the conventional binary thresholding-based boolean event generation in Discrete Event System (DES) for behavior activation. The multi-valued logic approach of FDES reduces the possibility of wrong event generation, which in turn decreases the possibility of inappropriate behavior selection. Experimental results using a physical robot are also presented to authenticate the feasibility of the proposed FDES-based object-pulling operation.

Abstract: This paper presents a novel intelligent sensory information processing technique using a fuzzy discrete event system (FDES) for robotic control. The proposed method combines the predictive control approach of a discrete event system with the approximate reasoning aspect of fuzzy logic. It develops a supervisory control strategy for behavior‐based robotic control using distributed FDES. The application of distributed FDES eliminates the formation of complex fuzzy predicates and a large fuzzy rule‐base. The FDES‐based approach also provides means for analyzing behavior‐based decision‐making using the observability and controllability of an FDES. The observability of an FDES describes uncertainties in sensory data, and the controllability of an FDES exploits uncertain state transitions in a dynamic environment. Comprehensive experiments on behavior‐based mobile robot navigation are presented to authenticate the performance of the proposed methodology.

This paper presents a novel behavior-modulation technique using a fuzzy discrete event system (FDES) for behavior-based robotic control. The method exploits the multivalued feature of fuzzy logic (FL) and event-driven property of a discrete event system (DES) to generate the activity of a behavior using fuzzy state vectors. State-based prediction of an activity is accomplished using fuzzily defined event matrices. A central arbiter employs priority-based arbitration among the activity state vectors and generates new event matrices to modify the activity states of the behaviors. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensory uncertainty and environmental dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. Real-time results of FDES-based mobile robot navigation are presented and compared against four different modulation methods to validate its superior performance

This paper proposes a novel algorithm combining fuzzy logic (FL) and genetic algorithm (GA) for concurrent mapping and localization (CML) of mobile robot. First, CML is formulated as a multidimensional informed search problem. The search is performed to detect a robot pose which can best accommodate the recent sensor scan in the currently available map. A fuzzy set theoretic approach is used to predict a sample based representation of the state space of possible robot poses and a GA is designed to find out the globally optimal solution from the predicted pose space. The GA evaluates the fitness of poses based on the sensory information and drives the generation gradually towards the globally optimal solution even when the fuzzy prediction is inaccurate. The best fit solution as decided by GA offers the most likely continuation of the currently available map. Experiment on synthetic and real data illustrates the robustness of the algorithm.

This paper presents a novel behavior modulation technique using fuzzy discrete event system (FDES) for mobile robots. The algorithm exploits the multi-valued feature of fuzzy logic (FL) and event-driven property of discrete event system (DES). Desirability (activity) of a behavior is expressed in terms of a fuzzy state vector. State-based prediction of the desirability is accomplished using fuzzy event matrices. A central arbiter employs priority based arbitration among the desirability state vectors and recommends new event matrices to modify the desirability states of the behaviors. The algorithm combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach constitutes state-based observability and controllability to cope with the uncertainties of sensory data and dynamic environment. Simulation results of FDES-based mobile robot navigation are also presented to validate the performance of the proposed algorithm.

This paper presents a novel approach for integrating global and local path planning of indoor mobile robots using motor schema based behavior coordination with fuzzy logic based adaptive behavioral modulation. First, Voronoi diagram approach is exploited to determine the global safe-path. Then A* search algorithm is employed to identify the Voronoi vertices (junctions or nodes) along the optimal path between the starting point of the robot and target position. Locally located junctions then act as subgoals for local motion planning. Motor schema based behavior fusion generates the safe direction of motion. Subgoals and points on the safe path exhibit attractive forces whereas obstacles cause repulsive forces. A supervisory fuzzy module is developed to incorporate adaptive behavioral capabilities which exploit the weighted decision-making based on contextual behavior composition. It dynamically produces the force coefficients for context dependent modulation of the virtual forces to generate local-minima free trajectory without oscillation. Adapted vector field histogram is deployed in order to modify the safe path in the presence of unknown obstacles. Simulation of indoor navigation examples validates the performance of the algorithm.

Fuzzy controller design includes both linear and non‐linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non‐linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non‐linear controller output are defined. A linear proportional‐integral‐derivative (PID) controller analogy is used for determining the linear tuning parameters. Non‐linear tuning is derived from the locally defined control properties in the non‐linear fuzzy output. The non‐linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non‐linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non‐linear parameters. In the second strategy the non‐linear parameters are initially selected on the basis of some desired non‐linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off‐line using an existing linear PID law and an adaptive non‐linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non‐linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.

A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properties than gain-scheduling controllers. Then, we evaluate the inference structures of direct-action controllers in five criteria: control-action composition, input coupling, gain dependency, gain-role change, and rule/parameter growth. Three types of fuzzy PID controllers, using one-, two- and three-input inference structures, are analyzed. The results, according to the criteria, demonstrate some shortcomings in Mamdani's two-input controllers. For keeping the simplicity feature like a linear PID controller, a one-input fuzzy PID controller with "one-to-three" mapping inference engine is recommended. We discuss three evaluation approaches in a nonlinear approximation study: function-estimation-based, generalization-capability-based and nonlinearity-variation-based approximations. The study focuses on the last approach. A nonlinearity evaluation is then performed for several one-input fuzzy PID controllers based on two measures: nonlinearity variation index and linearity approximation index. Using these quantitative indices, one can make a reasonable selection of fuzzy reasoning mechanisms and membership functions without requiring any process information. From the study we observed that the Zadeh-Mamdani's "max-min-gravity" scheme produces the highest score in terms of nonlinearity variations, which is superior to other schemes, such as Mizumoto's "product-sum-gravity" and "Takagi-Sugeno-Kang" schemes.

Fuzzy PID tuning requires two stages of tuning; low level tuning followed by high level tuning. At the higher level, a nonlinear tuning is performed to determine the nonlinear characteristics of the fuzzy output. At the lower level, a linear tuning is performed to determine the linear characteristics of the fuzzy output for achieving overall performance of fuzzy control. First, different fuzzy systems are defined and then simplified for two-point control. Non-linearity tuning diagrams are constructed for fuzzy systems in order to perform high level tuning. The linear tuning parameters are deduced from the conventional PID tuning knowledge. Using the tuning diagrams, high level tuning heuristics are developed. Finally, different applications are demonstrated to show the validity of the proposed tuning method.

Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporated with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria. Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, second-order, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation.

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.

In this work, we address an issue of nonlinearity variation evaluation for fuzzy PID controllers. This evaluation, or analysis, is important and basic in the light of nonlinear control design. The paper tries to investigate fuzzy controllers in regard to their nonlinearity variations when a finite number of tuning parameters are used. We suggest a nonlinearity variation index (NVI) for assessing the nonlinear design of the one-input fuzzy PID controllers developed by the authors. This index provides a quantitative measure to examine the nonlinearity freedom, or limitation, of the fuzzy systems. Using the NVI as a process-independent measure, designers are able to improve the design in the selections of the fuzzy rules, membership functions and inference schemes. Two differently-designed controllers are studied for the demonstration of how to use the NVI for an in-depth analysis of the nonlinear systems.

The fuzzy PID controller based on three-input inference is analyzed. Defining linear fuzzy regions in a linguistic error state space, a general linear-like fuzzy logic controller output is obtained. The solutions for twoand one-input fuzzy outputs are obtained as special cases of the three-input solution. The outputs of each controller are decomposed into linear and nonlinear terms with simple expressions. The multiphase solution that exists in the fuzzy output solution is expressed with least number of nonlinear terms. The linear output is used to identify the apparent linear PID gains of each controller type. The behavior of the apparent gains is similar to linear gain terms in a conventional PID controller. Thus three types of fuzzy controllers are tuned for obtaining overall performance.