Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
This provides a general approach for safe trajectory planning applicable to arbitrary robot models, addressing a domain-specific problem in robotics.
The paper tackles the unlabeled multi-robot motion planning problem with motion constraints in 2D obstacle-filled workspaces by formulating it as a multi-agent reinforcement learning problem with sparse global reward, and demonstrates its efficacy through varied simulations.
In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations.