MS2MP：一种用于运动规划的最小和消息传递算法
MS2MP: A Min-Sum Message Passing Algorithm for Motion Planning
摘要：
连续时间轨迹的高斯过程（GP）公式通过因子图的概率推理提供了运动规划问题的快速解决方案。然而，通常的解决方案收敛到不可行的局部极小值和规划的轨迹是不碰撞的。我们提出了一个消息传递算法，是更敏感的障碍，快速收敛时间。我们利用最小和消息传递算法的效用，在每个节点上执行本地计算来解决因子图上的推理问题。我们首先引入复合因子节点的概念，将因子图转化为线性结构图。接下来，我们开发了一种算法表示为最小和消息传递算法的运动规划（MS2MP），结合数值优化与消息传递，以找到无碰撞的轨迹。MS2MP在每个复合因子节点上进行数值优化求解非线性最小二乘最小化问题，然后利用因子图的线性结构，通过图节点间的消息传递计算完全图的最大后验概率（MAP）估计。每个复合节点的分散优化方法增加了对避免障碍物的敏感性，以解决更困难的规划问题。我们评估我们的算法进行广泛的实验，为示例性的运动规划任务的机器人操作器。我们的评估表明，MS2MP改善了现有的工作在收敛时间和成功率。
英文摘要：
Gaussian Process (GP) formulation of continuoustime trajectory offers a fast solution to the motion planning problem via probabilistic inference on factor graph. However, often the solution converges to in-feasible local minima and the planned trajectory is not collision-free. We propose a message passing algorithm that is more sensitive to obstacles with fast convergence time. We leverage the utility of min-sum message passing algorithm that performs local computations at each node to solve the inference problem on factor graph. We first introduce the notion of compound factor node to transform the factor graph to a linearly structured graph. We next develop an algorithm denoted as Min-sum Message Passing algorithm for Motion Planning (MS2MP) that combines numerical optimization with message passing to find collision-free trajectories. MS2MP performs numerical optimization to solve non-linear least square minimization problem at each compound factor node and then exploits the linear structure of factor graph to compute the maximum a posteriori (MAP) estimation of complete graph by passing messages among graph nodes. The decentralized optimization approach of each compound node increases sensitivity towards avoiding obstacles for harder planning problems. We evaluate our algorithm by performing extensive experiments for exemplary motion planning tasks for a robot manipulator. Our evaluation reveals that MS2MP improves existing work in convergence time and success rate.
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