Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot tasks. Dynamic obstacles may also be interactive, i.e., their behavior varies based on the behavior of other entities. We propose a novel representation for interactive behavior of dynamic obstacles and a decentralized real-time multi-robot trajectory planning algorithm allowing inter-robot collision and static and dynamic obstacle avoidance. Our planner simulates the behavior of dynamic obstacles during decision-making, accounting for interactivity. We account for the perception inaccuracy of static and prediction inaccuracy of dynamic obstacles. We handle asynchronous planning between teammates and message delays, drops, and re-orderings. We evaluate our algorithm in simulations using 25400 random cases and compare it against three state-of-the-art baselines using 2100 random cases. Our algorithm achieves up to 1.68x success rate using as low as 0.28x time in single-robot, and up to 2.15x success rate using as low as 0.36x time in multi-robot cases compared to the best baseline. We implement our planner on real quadrotors to show its real-world applicability.

翻译：在包含静态与动态障碍物的杂乱环境中实现无碰撞导航对许多多机器人任务至关重要。动态障碍物可能具有交互性，即其行为会因其他实体的行为而改变。我们提出了一种新颖的动态障碍物交互行为表示方法，以及一种去中心化实时多机器人轨迹规划算法，可实现机器人间无碰撞及静态、动态障碍物规避。该规划器在决策过程中模拟动态障碍物的行为，并考虑其交互特性。我们同时考虑了静态障碍物的感知误差与动态障碍物的预测误差，并处理了队友间的异步规划、消息延迟、丢包及乱序问题。通过25400个随机案例的仿真实验及与三种最新基线方法在2100个随机案例中的对比验证，实验结果表明：相比最优基线方法，本算法在单机器人场景中成功率最高提升1.68倍且耗时低至0.28倍，在多机器人场景中成功率最高提升2.15倍且耗时低至0.36倍。我们已在真实四旋翼无人机平台上部署该规划器，验证了其实际应用可行性。