  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.
