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.

翻译：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去中心化框架，能够在杂乱环境中实现实时规划。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，同时利用基于搜索的速度规划来逃离不可行初始值的局部最小值。随后，采用时空优化生成安全、平滑且动力学可行的轨迹。优化过程中，轨迹按固定时间步长离散化，通过对智能体间的符号距离施加惩罚实现碰撞规避，并结合前轮转向角限制的微分平坦性满足非完整约束。通过将轨迹广播至无线网络，智能体能够检测并预防潜在碰撞。我们在仿真和真实实验中验证了系统的鲁棒性。代码将以开源包形式发布。