A Message-Passing Algorithm for Multi-Agent Trajectory Planning


We describe a novel approach for computing collision-free global trajectories for p agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM) algorithm. 

December 5, 2013
Neural Information Processing Systems (NIPS) 2013



Jose Bento Ayres Pereira (Disney Research)

Nate Derbinsky (Disney Research)

Javier Alonso-Mora (Disney Research/ETH Joint PhD)

Jonathan Yedidia (Disney Research)

A Message-Passing Algorithm for Multi-Agent Trajectory Planning


Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments. We apply our method to classical challenging instances and observe that its computational requirements scale well with p for several cost functionals. We also show that a specialization of our algorithm can be used for local motion planning by solving the problem of joint optimization in velocity space.

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