论文标题
用高斯信念传播分发协作多机器人计划
Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation
论文作者
论文摘要
当许多机器人必须在狭窄的空间中一起工作时，可以通过向前时间窗口进行精确的协调计划，可以安全，高效的运动，但这通常需要对所有设备的集中控制，这很难扩展。我们演示了GBP Planning，这是一种基于高斯信念传播的多机器人计划问题的新型纯粹分布式技术，该技术由通用因子图制成，该技术由通用因子图制定，该图形在远期时间窗口上定义动态和碰撞约束。在模拟中，我们表明我们的方法允许高性能协作计划，在繁忙，复杂的场景中，机器人可以互相交叉。即使在沟通失败的情况下，它们也比替代分布式计划技术保持更短，更快，更光滑的轨迹。我们鼓励读者在https://youtu.be/8vsreujh610上查看随附的视频演示。
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.
