We consider the problem of safe multi-agent motion planning for drones in
uncertain, cluttered workspaces. For this problem, we present a tractable
motion planner that builds upon the strengths of reinforcement learning and
constrained-control-based trajectory planning. First, we use single-agent
reinforcement learning to learn motion plans from data that reach the target
but may not be collision-free. Next, we use a convex optimization, chance
constraints, and set-based methods for constrained control to ensure safety,
despite the uncertainty in the workspace, agent motion, and sensing. The
proposed approach can handle state and control constraints on the agents, and
enforce collision avoidance among themselves and with static obstacles in the
workspace with high probability. The proposed approach yields a safe, real-time
implementable, multi-agent motion planner that is simpler to train than methods
based solely on learning. Numerical simulations and experiments show the
efficacy of the approach.

我们提出一种可行的、基于强化学习和约束控制轨迹规划的无人机多智能体安全运动规划器，可以处理不确定的、杂乱的工作空间，并确保安全性、避免碰撞。该方法能够实时实施，相对于仅基于学习的方法，训练过程更简单，数值模拟和实验证明了该方法的有效性。