Precise coordinated planning over a forward time window enables safe and highly efficient motion when many robots must work together in tight spaces, but this would normally require centralised control of all devices which is difficult to scale. We demonstrate GBP Planning, a new purely distributed technique based on Gaussian Belief Propagation for multi-robot planning problems, formulated by a generic factor graph defining dynamics and collision constraints over a forward time window. In simulations, we show that our method allows high performance collaborative planning where robots are able to cross each other in busy, intricate scenarios. They maintain shorter, quicker and smoother trajectories than alternative distributed planning techniques even in cases of communication failure. We encourage the reader to view the accompanying video demonstration at https://youtu.be/8VSrEUjH610.

翻译：前向时间窗口内的精确协同规划能够使多个机器人在受限空间中安全高效地协同运动，但这通常需要集中控制所有设备，难以扩展。我们提出了GBP规划——一种基于高斯置信传播的全新纯分布式多机器人规划方法，通过定义前向时间窗口内动力学与碰撞约束的通用因子图进行建模。仿真结果表明，该方法能够实现高性能的协同规划，使机器人在复杂密集场景中实现交叉运动。即使在通信故障情况下，机器人的运动轨迹也比其他分布式规划方法更短、更平滑、更高效。我们建议读者观看配套视频演示（https://youtu.be/8VSrEUjH610）。