# Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

## TL;DR

This paper introduces a general reinforcement learning framework for safe, goal-oriented multi-robot planning in obstacle-rich environments, ensuring collision avoidance and adaptability to various robot models.

## Contribution

It presents a novel RL-based approach for multi-robot planning that guarantees collision-free trajectories using velocity obstacles, applicable to diverse robot dynamics.

## Key findings

- Effective collision avoidance demonstrated in simulations
- Applicable to arbitrary robot models
- Ensures smooth, safe trajectories

## Abstract

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.