Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances


  • Jingkai Chen Massachusetts Institute of Technology
  • Jiaoyang Li University of Southern California
  • Chuchu Fan Massachusetts Institute of Technology
  • Brian C. Williams Massachusetts Institute of Technology


Multiagent Planning


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.




How to Cite

Chen, J., Li, J., Fan, C., & Williams, B. C. (2021). Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11237-11245. Retrieved from



AAAI Technical Track on Multiagent Systems