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.
翻译:在本文中,我们展示了一种针对在2D,充满障碍的工作空间运行的无标签机器人的目标分配和轨迹规划的学习方法。更具体地说,我们解决了未贴标签的多机器人运动规划问题,将运动限制作为一个多剂强化学习问题,以一些稀疏的全球奖励。与以前为不同的机器人动态模型制定全新的手工制造优化成本或轨迹生成算法的工程相比,我们的框架是一种适用于任意机器人模型的一般方法。此外,我们通过使用速度障碍,设计了一个平稳的预测,保证所有机器人的邻居和障碍都能不受碰撞的轨迹。我们算法的效力通过多种模拟得到证明。