Motion planning with graph-based trajectories and Gaussian process inference
Motion planning with graph-based trajectories and Gaussian process inference
复制标题
使用基于图形的轨迹和高斯过程推理进行运动规划
DOI：
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发表时间：
2017
期刊：
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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：
Eric Huang;Mustafa Mukadam;Z. Liu;Byron Boots
作为轨迹优化的运动规划需要生成最小化期望目标函数或性能度量的轨迹。寻找全局最优解在实践中往往是棘手的：尽管存在快速运动规划算法，但大多数算法都容易陷入局部极小，这可能需要使用不同的初始化多次重新求解问题。在这项工作中，我们提出了一种新的运动规划算法GPMP-GRAPH，它考虑了一种基于图的初始化，同时探索多个同伦类，帮助解决局部极小问题。借鉴前人将连续时间轨迹表示为高斯过程(GP)样本的工作，并将运动规划问题描述为因子图上的推理，我们构造了一个相互关联的状态图，使得通过该图的每条路径都是有效的轨迹，并且可以在集合因子图上进行有效的推理。我们执行了各种基准测试，并表明我们的方法允许在一次评估一个轨迹所需的计算时间的一小部分内评估指数数量的轨迹，从而产生更彻底的解空间探索和更高的成功率。
Motion planning as trajectory optimization requires generating trajectories that minimize a desired objective function or performance metric. Finding a globally optimal solution is often intractable in practice: despite the existence of fast motion planning algorithms, most are prone to local minima, which may require re-solving the problem multiple times with different initializations. In this work we provide a novel motion planning algorithm, GPMP-GRAPH, that considers a graph-based initialization that simultaneously explores multiple homotopy classes, helping to contend with the local minima problem. Drawing on previous work to represent continuous-time trajectories as samples from a Gaussian process (GP) and formulating the motion planning problem as inference on a factor graph, we construct a graph of interconnected states such that each path through the graph is a valid trajectory and efficient inference can be performed on the collective factor graph. We perform a variety of benchmarks and show that our approach allows the evaluation of an exponential number of trajectories within a fraction of the computational time required to evaluate them one at a time, yielding a more thorough exploration of the solution space and a higher success rate.