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Predefined-Time H _∞ Cooperative Control for multi-robot systems Based on Adjustable Prescribed Performance Control and Adaptive Command Filter

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IEEE ACCESS 2025年 13卷 12055-12067页

作者： Liu, Haitao Li, Weichen Huang, Xin Tian, Xuehong Mai, Qingqun Guangdong Ocean Univ Shenzhen Inst Shenzhen 518120 Peoples R China Guangdong Ocean Univ Sch Mech Engn Zhanjiang 524088 Peoples R China Guangdong Engn Technol Res Ctr Ocean Equipment & M Zhanjiang 524088 Peoples R China

In this paper, a predefined-time H (infinity) cooperative controller with an adjustable prescribed performance function (PPF) and an adaptive command filter is proposed for multi-robot systems. First, an adjustable prescribed performance function is developed to limit the angular error and adaptively adjust the state convergence performance subject to actuator saturation, which effectively avoids the singularity problem. Second, a predefined-time adaptive command filter is designed to prevent "explosion of the complexity" problems and improve the convergence speed and filter precision. Third, a predefined-time H (infinity) control theory is developed to guarantee that the nonlinear system has global predefined-time stability and that the L-2 gain is less than gamma. Fourth, a predefined-time H-infinity cooperative controller for multi-robot systems (MRSs) is designed to achieve strong robustness to various disturbances. Finally, all the signals in the control system are bounded and converge within the predefined time, and the numerical simulation results verify the effectiveness and performance of the control system.

关键词： multi-robot systems adjustable prescribed performance function adjustable prescribed performance function predefined-time H-infinity control predefined-time adaptive command filter predefined-time adaptive command filter actuator saturation actuator saturation

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DEFORM: Adaptive Formation Reconfiguration of multi-robot systems in Confined Environments

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IEEE robotICS AND AUTOMATION LETTERS 2025年第5期10卷 4706-4713页

作者： Li, Jin Xu, Yang Shi, Xiufang Li, Liang Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

Achieving desired formation patterns without collisions is rather challenging for multi-robot systems in unknown obstacle-rich and confined environments, especially in narrow corridor scenes containing large-volume obstacles. To address this, we propose an adaptive formation reconfiguration method that can dynamically switch to the optimal formation pattern based on the current obstacle distribution. Specifically, we develop a novel obstacle-free maximum passable width detection method to formulate recursive optimization problems, which can determine the currently best formation shape and refine local goals away from obstacles. Then, we design a task assignment module for the temporary leader robot and a consensus-based distributed formation controller for each robot using model predictive control to ensure rapid convergence to the suggested formation shape. In addition, we utilize the potential field approach for each robot to improve collision avoidance. Extensive Gazebo simulations and real-world experiments in confined and obstacle-rich scenes verify the efficient formation convergence of our methods compared to the previous methods.

关键词： robots Collision avoidance Shape robot kinematics robot sensing systems multi-robot systems Formation control Switches Mobile robots Soft robotics collision avoidance formation control model predictive control underactuated robots

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Formation control and path planning of multi-robot systems via large language models

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Science China(Information Sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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Robust Neural Dynamics for Distributed Time-Varying Optimization With Application to multi-robot systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9598-9610页

作者： Wei, Lin Guan, Yongji Jin, Long Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

This paper develops a robust neural dynamics method for the distributed time-varying optimization problem with time-varying constraints. First, instead of assuming the objective functions and constraints to be static like a majority of the existing research, we consider distributed optimization from a time-varying perspective. Second, by employing the Lagrangian framework, we transform constraints and the concerned objective function that takes the summation of all local functions into a dynamic error function. Third, the robust neural dynamics method is capable of utilizing the time-varying information while solving constrained distributed optimization problems, and meanwhile handling disturbances purely based on its structure, thus lightening the communication and privacy burden. We provide proof of the convergence of the proposed method with activation functions under different disturbances. The comparative results on both illustrative examples and applications validate the efficiency. Note to Practitioners-The highlight of this paper lies in the co-design of time-varying computation and noise-tolerant ability for the proposed distributed optimization method, which could be beneficial to real-world scenarios. Different from existing methods focusing on static objective functions and constraints, we investigate how to find the trajectory formed by the time-varying optimal solutions even with the perturbation of noises. Besides, saturated or even nonconvex activation functions mimicking the synapses of the brain are incorporated into the proposed optimization methods to help accelerate the convergence and counteract the instability induced by noises, without heavily adding to the system's burden. We also provide theoretical proof to not only guarantee its capability but also guide how to set the involved parameters. This paper proposes a noise-tolerant method for accurately solving distributed time-varying optimization problems and validates its performance on a mu

关键词： Distributed time-varying optimization neural dynamics robustness multi-robot systems

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Queue Formation and Obstacle Avoidance Navigation Strategy for multi-robot systems Based on Deep Reinforcement Learning

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IEEE ACCESS 2025年 13卷 14083-14100页

作者： Gao, Tianyi Li, Zhanlan Xiong, Zhixin Wen, Ling Tian, Kai Cai, Kewei Dalian Minzu Univ Sch Mech & Elect Engn Dalian 116600 Peoples R China

In contemporary society, the widespread application of robotics across various domains emphasizes the critical role of robotic systems in performing tasks that are too dangerous or complex for humans. However, individual robots often struggle with these intricate tasks, necessitating the collaboration of multi-robot systems. This study proposes a novel deep reinforcement learning (DRL)-based method for achieving queue formation, obstacle avoidance, and navigation in multi-robot systems. The approach includes a specialized state representation and reward mechanism tailored to queue formation, where the state design incorporates the relative positions and actions of robots in the queue to ensure they maintain formation and avoid obstacles. The reward mechanism encourages robots to adjust their speed and position, preserving the queue structure even in complex environments. To address the challenges associated with initial DRL training, we integrate curriculum learning, which facilitates smoother and faster convergence by gradually increasing task difficulty. This approach enables the system to efficiently complete the training process and enhance performance. Upon successful formation training, a speed control mechanism is introduced to improve the stability of the queue during navigation, ensuring robots maintain uniform speed and formation while avoiding obstacles. Simulation results demonstrate the effectiveness of the proposed method in managing queue formations across various robot numbers and environmental complexities. The results highlight robust performance with up to 10 robots, maintaining effectiveness in both static and dynamic environments. However, performance limitations are observed with larger fleets, indicating the need for further advancements in collision avoidance and speed control.

关键词： robots robot kinematics robot sensing systems Collision avoidance Training Navigation multi-robot systems Deep reinforcement learning Velocity control Formation control multi-robot obstacle avoidance

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Decentralized Density Control of multi-robot systems Using PDE-Constrained Optimization

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IEEE robotICS AND AUTOMATION LETTERS 2025年第4期10卷 4045-4052页

作者： Niu, Longchen Notomista, Gennaro Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this letter, we propose a decentralized optimal density control strategy for multi-robot systems modeled as interacting Brownian particles. The robots' density dynamics are described by the Fokker-Planck equation, and an optimal control problem is formulated and solved accordingly. The stability of the decentralized controller is mathematically proven, and validated through simulations and experiments with a team of real brushbots. The results confirm the convergence of the proposed density-based decentralized optimal controller.

关键词： robots Mathematical models Noise robot sensing systems Stochastic processes Optimization Probability density function Location awareness Swarm robotics Convergence Distributed robot systems multi-robot systems optimization and optimal control

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A Distributed Framework for Integrated Task Allocation and Safe Coordination in Networked multi-robot systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11219-11238页

作者： Miele, Andrea Lippi, Martina Gasparri, Andrea Roma Tre Univ Dept Civil Comp Sci & Aeronaut Technol Engn I-00146 Rome Italy

Deploying a team of autonomous robots, operating collaboratively towards a common objective within dynamic environments, has the potential to improve the system efficiency across several fields. This paper proposes a distributed comprehensive framework enabling a networked multi-robot system to serve time-varying requests arising from different locations within the environment in a distributed and safe manner, i.e., by guaranteeing no collisions with possible obstacles and preserving connectivity among the robots. To this aim, a two-layer architecture is proposed where the top layer is in charge of distributively assigning new service requests to the robots by resorting to an auction-based algorithm, while the bottom layer is in charge of safely navigating the environment to serve the assigned requests by relying on Control Barrier Functions. However, the presence of connectivity constraints might affect the number of service requests that the multi-robot system can handle simultaneously and might lead to deadlock situations where robots cannot reach the designated locations due to loss of network connectivity. Hence, a distributed strategy based on consensus algorithms to detect and solve deadlocks in a distributed fashion is proposed. The completeness of the approach is proved. Simulation results in an agricultural setting and real-world laboratory experiments are provided to validate the effectiveness of the proposed *** to Practitioners-This paper was inspired by the necessity to coordinate a team of robots to perform tasks within an unstructured agricultural field, including both the decision-making and navigation strategies, with no central control unit as envisioned by the European project CANOPIES. To this aim, a distributed approach is designed where robots only rely on local data and information from neighboring robots to assign and execute tasks effectively in a coordinated manner. In addition, as working under local communication constraints ma

关键词： robots robot kinematics Collision avoidance System recovery robot sensing systems Resource management Navigation multi-robot systems Safety Automata Cooperative robots distributed control task allocation multi-robot systems

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Efficient Path Planning and Formation Control in multi-robot systems: A Neural Fields and Auto-Switching Mechanism Approach

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IEEE ACCESS 2025年 13卷 8270-8285页

作者： Fareh, Raouf Baziyad, Mohammed Rabie, Tamer Farouk Khadraoui, Sofiane Rahman, Mohammad Habibur Univ Sharjah Elect Engn Dept Sharjah U Arab Emirates Univ Sharjah RISE Sharjah U Arab Emirates Univ Sharjah Comp Engn Dept Sharjah U Arab Emirates Univ Wisconsin Milwaukee Mech Engn Dept Milwaukee WI 53211 USA

Current implementations of leader-follower systems in multi-robot environments typically separate path planning and formation control, often leading to inefficiencies in execution speed, path quality, and system stability. These systems rely on complex control strategies that struggle to adapt to dynamic and challenging environments, resulting in sub-optimal performance and potential collisions. This paper presents a novel approach to enhancing the path planning and formation control of multi-robot leader-follower systems by integrating neural fields and novel potential field modeling. The primary focus is on developing a unified platform that addresses the challenges of maintaining formation integrity, optimizing path quality, and ensuring real-time responsiveness. Neural Fields, which has gained significant traction in the past few years, utilizes fully connected neural networks to effectively encode continuous signals across various dimensions and resolutions. This growing interest in neural fields has highlighted their potential to provide solutions that are not only more precise and high-fidelity but also highly expressive and efficient in terms of memory usage. Thus, this work proposes leveraging neural fields to achieve high-quality path planning in significantly reduced time. By utilizing the expressiveness and memory efficiency of neural fields, the suggested approach aims to generate optimized paths quickly, making it well-suited for real-time applications where both accuracy and speed are critical. On the other hand, the follower robots are equipped with an auto-switching potential method, which intelligently toggles between attractive forces guiding the followers toward the leader and repulsive forces preventing collisions based on a mathematical model. The effectiveness of this approach is validated through experiments, demonstrating significant improvements in execution speed, path smoothness, and overall system stability compared to competitive method

关键词： robots robot kinematics Path planning Mathematical models Control systems Mobile robots Heuristic algorithms Neural networks multi-robot systems Formation control multi-robot leader-follower path planning potential field kinematic control

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Data-driven adaptive formation control based on preview mechanism for networked multi-robot systems with communication delays

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NEUROCOMPUTING 2025年 620卷

作者： Chao, Chenzhuolei Tan, Haoran Zhang, Xueming Wang, Gang Wu, You Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Xiangjiang Lab Changsha Peoples R China Hunan Univ Greater Bay Area Inst Innovat Guangzhou Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China

This study addresses the formation control task of networked multi-robot systems operating in an edge-end network architecture with communication delays. We propose a data-driven distributed adaptive formation controller DDAPM for stable and precise formation performance. Implemented at each edge-node, the DDAPM controller includes a single closed-loop angular velocity control module and a double closed-loop linear velocity control module connected in parallel. It integrates a preview mechanism to capture the intentions of robots and delay compensators to mitigate the adverse effects of communication delays. We analyze the stability and consensus of the NMRS utilizing DDAPM controllers. Numerical simulations are conducted to validate the feasibility, effectiveness, and adaptability of the DDAPM. Comparative simulations between the DDAPM and other controllers further substantiate its advantages. Additionally, an ablation study is performed to highlight the effectiveness and necessity of delay compensators and adaptive parameter pseudo-partial derivatives. Finally, we discuss the study's limitations and propose potential directions for future research.

关键词： multi-robot systems Networked control systems Data-driven control Formation control Model-free adaptive control

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Hierarchical Deep Reinforcement Learning for Computation Offloading in Autonomous multi-robot systems

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IEEE robotICS AND AUTOMATION LETTERS 2025年第1期10卷 540-547页

作者： Gao, Wen Yu, Zhiwen Wang, Liang Cui, Helei Guo, Bin Xiong, Hui Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Thust Artificial Intelligence Guangzhou 511453 Peoples R China

To ensure system responsiveness, some compute-intensive tasks are usually offloaded to cloud or edge computing devices. In environments where connection to external computing facilities is unavailable, computation offloading among members within an autonomous multi-robot system (AMRS) becomes a solution. The challenge lies in how to maximize the use of other members' idle resources without disrupting their local computation tasks. Therefore, this study proposes HRL-AMRS, a hierarchical deep reinforcement learning framework designed to distribute computational loads and reduce the processing time of computational tasks within an AMRS. In this framework, the high-level must consider the impact of data loading scales determined by low-level under varying computational device states on the actual processing times. In addition, the low-level employs Long Short-Term Memory (LSTM) networks to enhance the understanding of time-series states of computing devices. Experimental results show that, across various task sizes and numbers of robots, the framework reduces processing times by an average of 4.32% compared to baseline methods.

关键词： robots Graphics processing units Resource management Computational modeling Loading Processor scheduling Load modeling Delays Deep reinforcement learning Collaboration Computation offloading multi-robot systems reinforcement learning

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