Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs
Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs
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使用高斯过程和因子图进行运动规划作为概率推理
DOI：
10.15607/rss.2016.xii.001
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Byron Boots
中科院分区：
文献类型：
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作者：
Jing Dong;Mustafa Mukadam;F. Dellaert;Byron Boots
随着必须实时执行任务的高自由度机器人的使用越来越多，需要用于运动规划的快速算法。在这项工作中，我们从概率的角度来看运动规划。我们认为平滑的连续时间轨迹作为高斯过程（GP）的样本，并制定规划问题的概率推理。我们使用因子图和数值优化来快速执行推理，并展示了GP插值如何进一步提高算法的速度。我们的框架还允许我们逐步更新规划问题的解决方案，以应对不断变化的条件。我们基准我们的算法对最近的几个轨迹优化算法在多个环境中的规划问题。我们的评估表明，我们的方法比以前的算法快几倍，同时保持鲁棒性。最后，我们展示了我们的算法的增量版本的重新规划问题，并表明，它往往可以找到成功的解决方案，在一小部分的时间需要从头开始重新规划。
With the increased use of high degree-of-freedom robots that must perform tasks in real-time, there is a need for fast algorithms for motion planning. In this work, we view motion planning from a probabilistic perspective. We consider smooth continuous-time trajectories as samples from a Gaussian process (GP) and formulate the planning problem as probabilistic inference. We use factor graphs and numerical optimization to perform inference quickly, and we show how GP interpolation can further increase the speed of the algorithm. Our framework also allows us to incrementally update the solution of the planning problem to contend with changing conditions. We benchmark our algorithm against several recent trajectory optimization algorithms on planning problems in multiple environments. Our evaluation reveals that our approach is several times faster than previous algorithms while retaining robustness. Finally, we demonstrate the incremental version of our algorithm on replanning problems, and show that it often can find successful solutions in a fraction of the time required to replan from scratch.