Goal assignment and trajectory planning for large teams of interchangeable robots

This paper presents Goal Assignment and Planning: a computationally tractable, complete algorithm for generating dynamically feasible trajectories for $$N$$N interchangeable (identical) robots navigating through known cluttered environments to $$M$$M goal states. This is achieved by assigning goal states to robots to minimize the maximum cost over all robot trajectories. The computational complexity of this algorithm is shown to be polynomial in the number of robots in contrast to the expected exponential complexity associated with planning in the joint state space. This algorithm can be used to plan trajectories for dozens of robots, each in a potentially high dimensional state space. A series of planar case studies are presented and finally, experimental trials are conducted with a team of six quadrotor robots navigating in a constrained three-dimensional environment.

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