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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2023年第1期4卷 23-36页

作者： An, Xing Wu, Celimuge Lin, Yangfei Lin, Min Yoshinaga, Tsutomu Ji, Yusheng Univ Electrocommun Grad Sch Informat Engn Chofu Tokyo 1828585 Japan Univ Electrocommun Meta Networking Res Ctr Chofu Tokyo 1828585 Japan Inner Mongolia Normal Univ Coll Comp Sci Technol Hohhot 010011 Peoples R China Natl Inst Informat NII Informat Syst Architecture Res Div Tokyo 1018430 Japan

multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

关键词： robots robot kinematics multi-robot systems Decision making Task analysis Computer architecture Scalability Communication technology cooperative object transport multi-robot system platform survey

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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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Managing Conflicting Tasks in Heterogeneous multi-robot systems Through Hierarchical Optimization

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IEEE robotICS AND AUTOMATION LETTERS 2025年第6期10卷 5305-5312页

作者： De Benedittis, Davide Garabini, Manolo Pallottino, Lucia Univ Pisa Ctr Ricerca E Piaggio I-56122 Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

The robotics research community has explored several model-based techniques for multi-robot and multi-task control. Through constrained optimization, robot-specific characteristics can be taken into account when controlling robots and accomplishing tasks. However, in scenarios with multiple conflicting tasks, existing methods struggle to enforce strict prioritization among them, allowing less important tasks to interfere with more important ones. In this letter, we propose a novel control framework that enables robots to execute multiple prioritized tasks concurrently while maintaining a strict task priority order. The framework exploits hierarchical optimization within a model predictive control structure. It formulates a convex minimization problem in which all the tasks are encoded as linear equality and inequality constraints. The proposed approach is validated through simulations using a team of heterogeneous robots performing multiple tasks.

关键词： robots Optimization robot kinematics multi-robot systems Vectors Planning robot sensing systems Null space Computational efficiency Tuning Constrained motion planning motion and path planning multi-robot systems path planning for multiple mobile robots or agents

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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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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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Mean-Shift Shape Formation of multi-robot systems Without Target Assignment

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IEEE robotICS AND AUTOMATION LETTERS 2024年第2期9卷 1772-1779页

作者： Zhang, Yunjie Zhou, Rui Li, Xing Sun, Guibin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

The methods of shape formation in robot swarms are usually classified into two categories by whether assignment is used or not. The first is to use target assignment to assemble precise formation. However, the additional algorithm for re-assignment is required to handle unreasonable situations, which results in lower efficiency. The second, also called assignment-free method, is to use local behaviors to assemble formation, however, existing methods can rarely achieve the precise formation. In this letter, we present a distributed assignment-free algorithm to achieve the precise shape formation based on the mean-shift algorithm. Specifically, each target location in robot's perception range is equally regarded as a point of the mean-shift vector. Then, the weight value of each point is computed according to the density of the target location. Here, each robot obtains the density of the target location according to the distribution of its neighbors. Moreover, this density calculation also considers the states of non-neighboring robots via the hop-count algorithm, thus avoiding conflicts among robots. Subsequently, each robot can regard the calculated mean-shift vector as its control command. Finally, simulation results show that our algorithm can form precise shapes at least 8 times more efficient than the assignment-based approach and physical experiment results confirm that the proposed algorithm exhibits promising potential for practical applications.

关键词： Shape formation multi-robot systems mean-shift algorithm

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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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A Review of multi-robot systems and Soft robotics: Challenges and Opportunities

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SENSORS 2025年第5期25卷 1353-1353页

作者： Tejada, Juan C. Toro-Ossaba, Alejandro Lopez-Gonzalez, Alexandro Hernandez-Martinez, Eduardo G. Sanin-Villa, Daniel Univ Iberoamericana Ciudad Mexico Dept Engn Studies Innovat Prolongac Paseo Reforma 880 Mexico City 01219 Mexico Univ EIA Artificial Intelligence & Robot Res Grp IAR Envigado 055428 Colombia Univ Iberoamericana Ciudad Mexico InIAT Inst Appl Res & Technol Prolongac Paseo Reforma 880 Mexico City 01219 Mexico Univ EAFIT Sch Appl Sci & Engn Medellin 050022 Colombia

This review investigates the latest advancements in multi-robot systems (MRSs) and soft robotics, with a particular focus on their integration and emerging opportunities. An MRS extends principles from distributed artificial intelligence and coordination frameworks, enabling efficient collaboration in robotic applications such as object manipulation, navigation, and transportation. Soft robotics employs flexible materials and biomimetic designs to improve adaptability in unstructured environments, with applications in manufacturing, sensing, actuation, and modeling. Unlike previous reviews, which often address these fields independently, this work emphasizes their integration, identifying key challenges such as nonlinear dynamics, hyper-redundant configurations, and adaptive control. This review discusses recent advancements in locomotion, coordination, and simulation, offering insights into the development of adaptive and collaborative robotic systems across diverse applications.

关键词： multi-agent systems multi-robot systems soft robotics collaborative robotics systems integration

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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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