用机器学习优化多目标路径规划，兼顾动态约束与避障效率。
Combining Machine Learning and Sampling-Based Search for Multi-Goal Motion Planning with Dynamics
- 结合机器学习与旅行商算法构建成本矩阵，指导路径搜索顺序。
- 在复杂障碍环境中实现快速规划，支持车辆模型动态约束。
- 适合需高效遍历多个目标点的机器人导航任务。
本文研究在非结构化、障碍密集环境中的多目标运动规划问题，要求机器人在满足自身动力学约束的前提下，依次抵达多个目标区域并避免碰撞。为高效求解，该方法融合机器学习、旅行商问题（TSP）与基于采样的运动规划。通过向运动树添加无碰撞且符合动力学可行性的轨迹作为分支来扩展路径。对每个节点，使用TSP求解器计算剩余目标的访问顺序，依赖成本矩阵进行决策。关键创新在于利用机器学习构建成本矩阵，综合运行时预测与距离预测结果，用于单目标运动规划。路径扩展过程中优先选择具有低代价巡回路径的节点。在带有车辆模型的障碍密集环境中实验表明，该方法具有良好的计算效率和可扩展性。
原文摘要 · Abstract (English)
This paper considers multi-goal motion planning in unstructured, obstacle-rich environments where a robot is required to reach multiple regions while avoiding collisions. The planned motions must also satisfy the differential constraints imposed by the robot dynamics. To find solutions efficiently, this paper leverages machine learning, Traveling Salesman Problem (TSP), and sampling-based motion planning. The approach expands a motion tree by adding collision-free and dynamically-feasible trajectories as branches. A TSP solver is used to compute a tour for each node to determine the order in which to reach the remaining goals by utilizing a cost matrix. An important aspect of the approach is that it leverages machine learning to construct the cost matrix by combining runtime and distance predictions to single-goal motion-planning problems. During the motion-tree expansion, priority is given to nodes associated with low-cost tours. Experiments with a vehicle model operating in obstacle-rich environments demonstrate the computational efficiency and scalability of the approach.
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