Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
This work addresses the critical problem of safe and scalable multi-agent motion planning for autonomous systems operating in complex environments, which is an incremental improvement over existing methods.
This paper presents a scalable and effective multi-agent safe motion planner for agents with nonlinear dynamics and bounded disturbances. It finds piecewise linear paths for each agent, guaranteeing collision avoidance with obstacles and other agents, and demonstrates improvements in solving time and solution quality compared to two state-of-the-art planners.
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. We address this problem by finding a piecewise linear path for each agent such that the actual trajectories following these paths are guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial tracking error of the actual trajectories of the controlled agents can be pre-computed for any qualified path that considers the minimum duration of each path segment due to actuation limits. Using these bounds, we find a collision-free path for each agent by solving Mixed Integer-Linear Programs and coordinate agents by using the priority-based search. We demonstrate our method by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors, respectively, and show improvements over the solving time and the solution quality compared to two state-of-the-art multi-agent motion planners.