In this paper, we propose a novel end-to-end approach for solving the multi-goal path planning problem in obstacle environments. Our proposed model, called S&Reg, integrates multi-task learning networks with a TSP solver and a path planner to quickly compute a closed and feasible path visiting all goals. Specifically, the model first predicts promising regions that potentially contain the optimal paths connecting two goals as a segmentation task. Simultaneously, estimations for pairwise distances between goals are conducted as a regression task by the neural networks, while the results construct a symmetric weight matrix for the TSP solver. Leveraging the TSP result, the path planner efficiently explores feasible paths guided by promising regions. We extensively evaluate the S&Reg model through simulations and compare it with the other sampling-based algorithms. The results demonstrate that our proposed model achieves superior performance in respect of computation time and solution cost, making it an effective solution for multi-goal path planning in obstacle environments. The proposed approach has the potential to be extended to other sampling-based algorithms for multi-goal path planning.

翻译：本文提出了一种新颖的端到端方法，用于解决障碍环境中的多目标路径规划问题。所提出的模型S&Reg将多任务学习网络与TSP求解器及路径规划器相结合，能够快速计算出遍历所有目标的闭环可行路径。具体而言，该模型首先通过分割任务预测可能包含连接两目标最优路径的潜力区域。同时，神经网络以回归任务对目标间成对距离进行估计，其结果构建出用于TSP求解器的对称权重矩阵。借助TSP结果，路径规划器在潜力区域引导下高效探索可行路径。我们通过仿真实验对S&Reg模型进行了全面评估，并将其与其他基于采样的算法进行比较。结果表明，本文模型在计算时间与解质量方面均展现出优越性能，为障碍环境下的多目标路径规划提供了有效解决方案。该方法具有推广至其他基于采样的多目标路径规划算法的潜力。