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.

翻译：本文研究在非结构化、障碍物密集环境中的多目标运动规划问题，要求机器人在避免碰撞的同时到达多个目标区域。规划的运动还需满足机器人动力学所施加的微分约束。为高效求解，本文综合利用机器学习、旅行商问题（TSP）方法与基于采样的运动规划技术。该方法通过添加无碰撞且动力学可行的轨迹作为分支来扩展运动树。利用TSP求解器为每个节点计算访问剩余目标的顺序路径，该过程依赖于一个成本矩阵。本方法的核心特点是借助机器学习构建该成本矩阵，其通过融合单目标运动规划问题的运行时间预测与距离预测来实现。在运动树扩展过程中，优先处理与低成本路径相关联的节点。通过在障碍物密集环境中运行的车辆模型实验，验证了该方法在计算效率与可扩展性方面的优势。