Ground-to-air communication is a critical technology for establishing an Internet of Things (IoT) network system, especially in emergency situations. We are investigating the trajectory planning problem of a data collection IoT network assisted by an unmanned aerial vehicle (UAV). This article aims to solve the data collection Dubins traveling salesman problem (DCDTSP) for UAVs in a three-dimensional and complex obstacle environment. To optimize the paths for UAVs in data collection from terminals to UAVs, a novel releasing-collecting-recycling (RCR) framework has been established for heterogeneous multi-UAVs. In the UAV release step, we propose a multi-height hierarchical target clustering (MHTC) algorithm to enhance the efficiency of multi-target clustering. In the data collection step, a bundling ant colony system (BACS) is developed to minimize the length of the obstacle avoidance path while still meeting the communication throughput constraint. Meanwhile, the dynamic adaptive window probabilistic roadmap (DAWPRM) algorithm has been enhanced to address the obstacle avoidance distance in BACS. In the UAV recycling step, we propose a time synchronous Dubins recycling strategy to plan the simultaneous arrival trajectory for multiple UAVs with a constrained turning radius. The results of simulation experiments showed that the proposed RCR framework is optimal for finding Pareto solutions for DCDTSP.

Open-vocabulary mobile manipulation (OVMM) that involves the handling of novel and unseen objects across different workspaces remains a significant challenge for real-world robotic applications. In this paper, we propose a novel Language-conditioned Open-Vocabulary Mobile Manipulation framework, named LOVMM, incorporating the large language model (LLM) and vision-language model (VLM) to tackle various mobile manipulation tasks in household environments. Our approach is capable of solving various OVMM tasks with free-form natural language instructions (e.g. "toss the food boxes on the office room desk to the trash bin in the corner", and "pack the bottles from the bed to the box in the guestroom"). Extensive experiments simulated in complex household environments show strong zero-shot generalization and multi-task learning abilities of LOVMM. Moreover, our approach can also generalize to multiple tabletop manipulation tasks and achieve better success rates compared to other state-of-the-art methods.

This paper studies a fractional-order control and stability scheme for a class of n-DOF mechanical systems that are subject to actuator saturation and unknown disturbances. By the fractional-order Leibniz rule, a generalized fractional-order Lyapunov stability (FOLS) method is developed to support the design of a class of fractional-order robust controllers, which can guarantee the uniform ultimate boundedness and faster convergence. Based on this, a fractional-order backstepping-like feedback controller (FOBFC) is raised to realize an effective and stable control performance of mechanical systems, which can ameliorate the shortages of the traditional backstepping control effectively. Moreover, a set of fractional-order methods, including actuator compensation (FOAC) and disturbance observer (FODO), are proposed and involved in FOBFC to handle the actuator saturation and unknown time-varying disturbances, making it possible to achieve a more robust and stable control performance. Finally, numerical simulations and comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed fractional-order control scheme, in n-DOF mechanical systems. Note to Practitioners—A fractional-order control and stability scheme is developed to regulate a class of practical n-DOF mechanical systems, which is composed of FOBFC, FOAC and FODO. It can be applied to deal with the actuator saturation and time-varying disturbance of n-DOF mechanical systems, contributing to realizing a more robust and stable control performance. In the application, a Euler-Lagrange dynamics of mechanical systems is required for constructing the fractional-order tracking errors. Subsequently, FOBFC law can be raised based on the generalized FOLS, to guarantee the asymptotic convergence and stability of mechanical systems. To address the practical issues of mechanical systems, FOAC and FODO are necessary for the proposed control scheme to compensate for the actuator saturation adaptively, and handle the unknown and time-varying disturbances effectively.

Dynamics-based control offers a promising approach to exploring the motion potential of soft robots. However, inherently infinite degrees of freedom of these systems pose significant challenges for dynamics modeling, closely followed by the pressing robustness concerns arising from finite-dimensional approximations. This paper addresses these issues by proposing a physics-informed dynamics learning neural network and an adaptive fractional-order control for continuum soft robots. Specifically, a deep Lagrangian neural network is first developed with an embedded self-attention mechanism to enhance learning efficiency, accuracy, and data sensitivity. Subsequently, an adaptive fractional-order sliding mode controller is designed, leveraging the inherent historical memory properties of fractional calculus. This controller not only ensures robust shape control but also improves response speed and tracking accuracy. To further handle model discrepancies in the learned dynamics and external disturbances, a nonlinear disturbance observer is introduced to effectively estimate and compensate for lumped uncertainties, thereby ensuring reliable performance. Theoretical analysis confirms the closed-loop stability, while both simulation and experiment results validate the high dynamics fitting accuracy of the proposed network, as well as the robust and precise tracking capability of the fractional-order controller. Note to Practitioners—Soft robots offer great potential in unstructured or constrained environments owing to their compliance and adaptability. However, their high degrees of freedom and nonlinear behaviors make analytical modeling and robust control particularly challenging. Meanwhile, traditional closed-box learning methods often suffer from limited physical interpretability, reliability and extrapolability. This work presents a physics-informed dynamics learning framework combined with a fractional-order controller for soft robots. The dynamics learning network embeds physical priors to enhance model interpretability and extrapolability, while a self-attention mechanism improves data efficiency and modeling accuracy. Additionally, a disturbance observer is designed to estimate and compensate for model discrepancies and external disturbances, thereby contributing to the system’s robustness. Incorporating the observer’s outputs, the adaptive fractional-order controller further enhances closed-loop behavior by leveraging the memory properties of fractional calculus.

In multi-vehicle systems, the coupling problem between the task allocation and path planning and the variability of task execution solutions creates challenges for utility estimation and affects the effectiveness of distributed mission planning. To characterize the effect of task sequences on the task utilities and implement a task-extended distributed allocation, we propose a task-extended utility tensor algorithm (TEUTA) based on market mechanism. In the mission planning problem of multi-vehicle system, we consider the impact of the task schedule on the vehicle trajectory, and indicate the vehicle task execution utilities under different preceding task points in the form of tensors. Further, a task-extended utility tensor iterative algorithm (TEUTIA) is presented based on an iterative strategy to improve the algorithm in terms of computational complexity. A task execution utility estimation model and an algorithm framework are designed for the implementation of the two proposed algorithms. The simulation and experimental results show that compared with the non-tensor method, TEUTA and TEUTIA can achieve higher task execution performance, and TEUTIA has better computational efficiency. Note to Practitioners—This work presents two novel multi-vehicle distributed mission planning algorithms based on the market mechanism. TEUTA and TEUTIA proposed in this paper can be applied to address the impact of vehicle motion constraints on mission planning, which are widely present in various types of common nonholonomic vehicles such as two-wheel differential drive vehicles and Ackerman steering vehicles. In the application of the algorithms, an accurate kinematic model of the vehicles is required for trajectory planning to estimate the task execution reward precisely, which is necessary for effective mission planning. When the vehicle trajectory planning algorithm is computationally intensive, TEUTIA can significantly reduce the computational consumption and improve the mission planning efficiency compared with TEUTA without losing task execution reward. Finally, a stable inter-vehicle communication network is required for the interactive process of the market mechanism, where bi-directional communication exists between any two vehicles, to ensure the stability of mission planning.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.

This article examines the problems of task allocation and path optimization for multiobjective coverage in a congestion risk environment with obstacle constraints. An improved probabilistic roadmap (PRM*) algorithm is proposed, which eliminates the zig-zag paths around the path endpoints. The$K$-distance PRM*$(K$-DPRM*) provides a novel clustering metric for task allocation in an obstacle environment. An ant colony system-PRM* (ACS-PRM*) algorithm is proposed to solve the congestion avoidance traveling salesman problem (CATSP) by voyage optimization of multiobjective coverage. Additionally, the mapping relationship between the probability of environmental congestion and the velocity of robot is established and combined with the feedforward control method to improve the motion control of robots. Simulations and experiments verify the effectiveness of the path optimization method in obstacle environments with congestion risk.

There exist two problems: 1) the model uncertainties caused by flexible cable and 2) the oscillations caused by the asynchronous adjustment of multiple cable lengths, which make it difficult to achieve accurate tracking control of the end-effector (EE) for cable-driven parallel robots (CDPRs) in practical applications. This paper addresses a non-singular terminal sliding mode control scheme with the relative-coupling synchronization in the cable space (NTSM-RSC) to overcome the effect of the model uncertainties and suppress the oscillations of cables simultaneously. A non-singular terminal sliding mode controller (NTSMC) is proposed to enhance the robustness of the system to model uncertainties. A relative-coupling error vector of multiple cable lengths is established based on ring topology, and a relative-coupling synchronization controller in the cable space (RSC) is proposed to improve the synchronization of multiple cable lengths. The RSC is added to the NTSMC to improve the synchronization of cables compared with the NTSMC, namely the NTSM-RSC. The finite-time convergence of the error of system is theoretically guaranteed. The effectiveness and superiority of the NTSM-RSC are verified by experiments.

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

Many approaches have been influential in the robotics field because of deep learning (DL). As space robots need more reliability and stability, model-free algorithms with deep learning have particular advantages over the traditional methods in space environment. In this paper, we present an original robot current/torque prediction based on robot dynamic system with deep learning. Also, we add sliding mode-based fixed-time controller to improve the control performance. It has analysed manipulator current information through robot dynamic property’s matrix nature from fewer samples. This method has significant benefits in terms of robot current/torque identification and tracking. It also performs well in robustness and learning rates. This generic method has developed to solve a variety of problems using deep learning and data filtering with manipulator dynamics process, which includes deep learning with fractional order differential operators, robot dynamics and Kalman smoothing. We verified our algorithm into a real two-joint space robot on air-floating platform in zero gravity environment. The final results show it can learn to predict current/torque based on robot dynamics and complete the finitetime convergence. This paper made several key contributions to the fields of current/torque identification and prediction with manipulator dynamics and deep learning in space robot models. It performs very well in robot current/torque tracking and predicting new situations.

To handle the common existing constraints, that is, limited energy supplies and limited communication bandwidth in multiagent systems (MASs), this article investigates the consensus problem in MASs with event-triggered communication (ETC) and state quantization. In order to compensate for the effect brought by mismatched disturbances, we also propose a novel multiple discontinuous sliding-mode surface, and the corresponding sliding-mode control law is constructed by considering the event-triggered and dynamic quantized mechanisms jointly. Under such a scheme, it is shown that the state trajectories of all the agents will be regulated to achieve consensus asymptotically and the Zeno behavior can be avoided completely. We further extend this work to self-triggered and periodic event-triggered cases. Particularly, in a periodic event-triggered approach, the new form of triggering conditions and upper bound of the sampling periods are provided explicitly. As a result, all agents can reach bounded consensus. Moreover, the upper bound of the consensus error can be arbitrarily adjusted by appropriately selecting parameters, and the periodic event-triggered case will be reduced to the event-triggered case when the bound approaches 0 (sampling periods approach 0 at the same time). A numerical example is illustrated to verify the effectiveness of the proposed algorithms.

This paper examines a trajectory homotopy optimization framework for multiple unmanned aerial vehicles (multi-UAV) to solve the problem of dynamic penetration mission planning (PMP) with hostile obstacles and perception constraints. Constrained problems are usually more challenging and difficult to solve with some practical constraints and requirements. To improve the efficiency of the solution for the penetration path, a novel variable-time mechanism has been constructed to adapt to the updated delay time of unknown target search (UTS) and dynamic trajectory planning (DTP) two stages. The occupancy grid maps are established by a Gaussian probability field (GPF) for predicting the positions of enemy UAVs. To fully consider the hostile obstacle constraint, a hybrid adaptive obstacle avoidance approach dynamic window PRM (DW-PRM) is designed to shorten the planned path. The penetration strategy algorithm (SG) is developed based on the proposed strategy set and decision tree. To improve the ability of dynamic obstacle avoidance, the multiple coupled penetration homotopy trajectory is addressed with a turning radius constraint. The simulation results indicated that the penetration homotopy framework for multi-constraints can solve the multi-UAV PMP problem.

In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution, which we term TOpology Preserving Self-Organizing Map (TOPSOM). TOPSOM well preserves the topology of the node map to be traversed by maintaining the continuity of nodes and the distances between them. In addition, to satisfy the requirements of convex hull, we design an elastic competitive Hebbian learning rule. TOPSOM can solve large-scale TSPs with high precision and high efficiency with limited computational costs. Extensive experimental results on mainstream route planning benchmarks including TSPLIB and National TSP’s show that our method consistently outperforms baseline methods, by up to 7.7% in terms of the Percent Deviation of Mean solution to best known solution.

The environment of most real-world scenarios such as malls and supermarkets changes at all times. A pre-built map that does not account for these changes becomes out-of-date easily. Therefore, it is necessary to have an up-to-date model of the environment to facilitate long-term operation of a robot. To this end, this paper presents a general lifelong simultaneous localization and mapping (SLAM) framework. Our framework uses a multiple session map representation, and exploits an efficient map updating strategy that includes map building, pose graph refinement and sparsification. To mitigate the unbounded increase of memory usage, we propose a map-trimming method based on the Chow-Liu maximum-mutual-information spanning tree. The proposed SLAM framework has been comprehensively validated by over a month of robot deployment in real supermarket environment. Furthermore, we release the dataset collected from the indoor and outdoor changing environment with the hope to accelerate lifelong SLAM research in the community. Our dataset is available at https://github.com/sanduan168/lifelong-SLAM-dataset.

This article investigates the problem of optimal tracking control for cyber-physical systems (CPSs) when the cyber realm is attacked by Denial-of-Service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and analyze the impact of DoS attacks on tracking performance. First, a Riccati equation for the augmented system, including the system model and the reference model is derived under the framework of dynamic programming. The existence and uniqueness of its solution are proved. Second, the impact of the successful DoS attack probability on tracking performance is analyzed. A critical value of the probability is given, beyond which the solution to the Riccati equation cannot converge. The tracking controller cannot be designed. Third, reinforcement learning is introduced to design the optimal tracking control schemes, in which the system dynamics are not necessary to be known. Finally, both a dc motor and an F16 aircraft are used to evaluate the proposed control schemes in this article.

In this paper, a high-order nonlinear disturbance observer-based fractional-order sliding mode control (FOSMC) strategy is proposed for DC/DC buck converters in the presence of mismatched disturbances, ensuring the properties of both stability and dynamic performance. Traditional sliding mode control can deal with matched disturbances while achieving desired closed-loop performance, unfortunately it is particularly sensitive to mismatched disturbances. In the proposed control structure, two nonlinear disturbance observers are constructed to estimate both matched and mismatched disturbances in finite time. Then, the estimated variables are used for the fractional-order sliding mode surface design, where a super twisting sliding mode controller is designed to drive the states of the system to track their desired values. Comparing with the traditional SMC, the proposed method not only reduces the sensitivity of the system to mismatched disturbance, but also improves the transient performance of the system. Simulation and experimental results have comprehensively illustrated the feasibility and effectiveness of the proposed strategy.

In this paper, three different models of friction in presliding regime on linear motor platform are deduced according to basic physical laws, which are named as the Kelvin model, the modified Kelvin model and the four-element model. To validate the effectiveness of these models, the particle swarm optimization is applied to determine the coefficients in each model. Then experiments of ramp input force and step-like input force are conducted to collect the tiny displacement of the moving stage; simulations are carried out to calculate the models under input forces. Through analysis on the error and comparison between experiments and simulations, we can draw a conclusion that the modified Kelvin model is the most precise friction model on linear motor platforms.

In this paper, a robust level set method is proposed for image segmentation. Traditional level set methods are sensitive to noise in images which greatly limits its application in real project. To overcome this shortcoming, the fractional order regularization and Markov random fields term are incorporated into the traditional level methods in this paper. The fractional order regularization can reveal more details of the image and the Markov random field (MRF) term takes the hole image into account. In additional to these two terms, a region term and a penalty term are added into the energy function. The comparison of the proposed method with the classical level set method is made and the results show that the proposed method is robust to noise in images in image segmentation application.

Software developers often find answers to their programming issues on the Internet. Q&A (Question & Answer) websites has been becoming more and more popular. Among the available technical Q&A sites, the most prevalent one is the Stack Overflow, it has been becoming one of the invaluable knowledge repositories. The development trends of language and mainstream operation systems are discussed by using the SVD (Singular Value Decomposition) and K-means algorithm in this paper. Some key findings include: Traditional programming languages such as C#, C++, C develop slowly and the light script languages such as Php, JavaScript and Python develop rapidly, traditional operation systems such as Windows and Linux develop slowly and the mobile devices operation systems such as IOS and Android develop rapidly. Through studying and analyzing the development of mainstream technology, people can grasp the dynamic situation of the software field, which has important and far-reaching significance to guide the work of software engineering.

A novel full-order sliding mode tension control scheme for the deployment/retrieval of the space tether system is proposed. The deployment/retrieval dynamics of the space tether system are derived by using Lagrangian mechanics theory. The ideal full-order sliding mode surfaces of the deployment/retrieval dynamics are design using KTC and the second method of Lyapunov, and the designed control technologies can guarantee the asymptotic stability of the full-order sliding mode dynamics. The continuous input is applied to ensure that the system states can reach the ideal surfaces in finite time and keep stable in the subsequent time. The positive tension limit is taken into consideration with choosing appropriate parameters or gains in the design of the full-order sliding mode controller. The numerical results valid the effectiveness of the proposed methods.

This paper is concerned with asynchronous H∞state-feedback stabilization problem for a class of switched linear systems with detection delays. Without lose of generality, the switched signal is considered to satisfy dwell time switching. Time-varying overlapped detection delay, which is allowed to be overlapped with others and is much more challenging than the detection delay of overlapped-free, is introduced. By establishing the stability of the considered autonomous system, a controller is designed such that the closed-loop nominal system is globally uniformly exponentially stable. H∞stabilization controller is further designed for the considered system with additive disturbance. A numerical example illustrates that the theoretical results are excellent.

The problem of decentralized piecewise ℋ∞control for discrete-time large-scale nonlinear systems with time-varying delay is studied. Each nonlinear subsystem in the large-scale system is represented by a Takagi-Sugeno (T-S) model, and the time-varying state delay of each subsystem is assumed to be of an interval-like type. We propose a two-term approximation method to transform the closed-loop fuzzy control system into an interconnected formulation, which is subject to two constant time delays in the forward path and norm-bounded uncertainties in the feedback one. By introducing a piecewise Lyapunov-Krasovskii functional (PLKF) and utilizing the scaled small gain (SSG) theorem, the LMI conditions to the decentralized piecewise memory ℋ∞state-feedback controller design for the large-scale fuzzy systems are derived. An example is provided to verify the efficacy of the proposed method.