分布式安全协作向量场用于轨迹曲率约束的多机器人系统
Distributed Safe Cooperative Vector Field for Trajectory Curvature Constrained Multi-Robot Systems
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中文总结 AI 辅助
针对多机器人轨迹曲率约束问题，提出分布式安全协作向量场方法，结合协作与避碰向量场，仅需邻居单个虚拟变量实现协作与避障，经仿真和实验验证。
中文摘要 AI 辅助
轨迹曲率约束是实际多机器人系统中固有的问题，因为机器人的转向能力有限。如果不适当考虑这些约束，机器人可能无法完成分配的任务，其轨迹可能偏离预期路径。本文针对受轨迹曲率约束的多机器人系统，提出了一种分布式安全协作向量场方法。所提出的方法由协作向量场和面向安全的避碰向量场组成，旨在解决多机器人路径跟踪任务中的协作运动和安全避碰问题。开发了一种具有自适应可调反应边界的面向安全的避碰向量场，以适应机器人的运动学曲率约束，从而确保避碰机动在物理上的可行性。所提出的向量场仅需来自每个相邻机器人的单个虚拟变量即可实现协作运动，并确保避障和机器人间避碰。通过仿真和在实际多机器人平台上的真实实验验证了所提出方法的有效性。
英文摘要
Trajectory curvature constraints are inherent in practical multi-robot systems due to the limited turning capabilities of the robots. Without properly accounting for these constraints, robots may fail to accomplish assigned tasks, and their trajectories may diverge from the intended paths. This paper proposes a distributed safe cooperative vector field approach for multi-robot systems subject to trajectory curvature constraints. The proposed approach is composed of a cooperative vector field and a safety-oriented collision avoidance vector field, aiming to address the problems of cooperative motion and safe collision avoidance in multi-robot path-following tasks. A safety-oriented collision avoidance vector field with adaptively adjustable reactive boundary is developed to accommodate the kinematic curvature constraints of robots, thereby ensuring the physical feasibility of collision avoidance maneuvers. The proposed vector field requires only a single virtual variable from each neighboring robot to achieve cooperative motion and ensure both obstacle avoidance and inter-robot collision avoidance. The effectiveness of the proposed approach is validated through both simulations and real-world experiments on an actual multi-robot platform.
发表机构
- Hunan University（湖南大学）
- National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。
