In this article, we consider the problem of safe multiagent 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 (RL) and constrained-control-based trajectory planning. First, we use single-agent RL 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, multiagent motion planner that is simpler to train than methods based solely on learning. Numerical simulations and experiments show the efficacy of the approach.

中文翻译：

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使用过滤强化学习在无人机不确定性下进行安全多智能体运动规划

在本文中，我们考虑了在不确定、杂乱的工作空间中无人机的安全多智能体运动规划问题。对于这个问题，我们提出了一种易于处理的运动规划器，它建立在强化学习（RL）和基于约束控制的轨迹规划的优势之上。首先，我们使用单代理强化学习从到达目标但可能并非无碰撞的数据中学习运动计划。接下来，我们使用凸优化、机会约束和基于集合的方法进行约束控制，以确保安全，尽管工作空间、代理运动和传感存在不确定性。所提出的方法可以处理代理的状态和控制约束，并以高概率强制避免代理之间以及与工作空间中的静态障碍物发生碰撞。所提出的方法产生了一个安全、实时可实现的多智能体运动规划器，它比仅基于学习的方法更容易训练。数值模拟和实验表明了该方法的有效性。