Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments

TL;DR

This paper presents a decentralized planning framework for car-like robotic swarms that enables real-time, safe, and smooth navigation in cluttered environments through topology-guided path finding and spatial-temporal optimization.

Contribution

It introduces a novel decentralized approach combining topology-aware path planning, search-based speed optimization, and spatial-temporal trajectory generation for car-like robots in cluttered spaces.

Findings

System is robust in simulation and real-world tests.

Achieves collision-free, smooth, and feasible trajectories.

Enables real-time decentralized coordination among agents.

Abstract

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.