杂乱环境中类车机器人集群的分散式规划
Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments
AI总结：
本文提出一种面向类车机器人集群的分散式规划框架，结合拓扑引导路径搜索、基于搜索的速度规划与时空优化，实现杂乱环境中的实时避碰与动力学可行轨迹生成，并通过仿真和实机实验验证其鲁棒性。
摘要：
机器人集群是机器人研究领域的热点。本文提出了一种面向类车机器人集群的分散式框架，能够在杂乱环境中进行实时规划。在该系统中，路径搜索由环境拓扑信息引导，以避免频繁的拓扑变化，并利用基于搜索的速度规划来摆脱不可行初始值的局部极小值。随后采用时空优化生成安全、平滑且动力学可行的轨迹。在优化过程中，轨迹按固定时间步长离散化。通过对智能体之间带符号距离施加惩罚来实现避碰，差分平坦性与前轮转向角限制相结合满足非完整约束。通过将轨迹广播到无线网络，智能体能够检查并预防潜在碰撞。我们在仿真和真实世界实验中验证了系统的鲁棒性。代码将作为开源软件包发布。
英文摘要：
Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments. In this system, path finding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leveraged to escape from infeasible initial value's local minima. Then spatial-temporal optimization is employed to generate a safe, smooth and dynamically feasible trajectory. During optimization, the trajectory is discretized by fixed time steps. Penalty is imposed on the signed distance between agents to realize collision avoidance, and differential flatness cooperated with limitation on front steer angle satisfies the non-holonomic constraints. With trajectories broadcast to the wireless network, agents are able to check and prevent potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
