DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor

Abstract

This article presents an online distributed trajectory planning algorithm for a quadrotor swarm in a maze-like dynamic environment. We utilize a dynamic linear safe corridor to construct the feasible collision constraints that can ensure interagent collision avoidance and consider the uncertainty of moving obstacles. We introduce mode-based subgoal planning to resolve deadlock faster in a complex environment using only previously shared information. For dynamic obstacle avoidance, we adopt heuristic methods such as collision alert propagation and escape point planning to deal with the situation where dynamic obstacles approach the agents clustered in a narrow corridor. We prove that the proposed algorithm guarantees the feasibility of the optimization problem for every replanning step. In an obstacle-free space, the proposed method can compute the trajectories for 60 agents on average 7.66 ms per agent with an Intel i7 laptop and shows the perfect success rate. Also, our method shows 64.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter flight time than buffered Voronoi cell and 34.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter than with our previous work. We conduct the simulation in a random forest and maze with four dynamic obstacles, and the proposed algorithm shows the highest success rate and shortest flight time compared to state-of-the-art baseline algorithms. In particular, the proposed algorithm shows over 97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate when the velocity of moving obstacles is below the agent's maximum speed. We validate the safety and robustness of the proposed algorithm through a hardware demonstration with ten quadrotors and two pedestrians in a maze-like environment.