S&Reg: End-to-End Learning-Based Model for Multi-Goal Path Planning Problem

TL;DR

This paper introduces S&Reg, an end-to-end neural network model combining segmentation, regression, and TSP solving to efficiently generate feasible multi-goal paths in obstacle environments, outperforming existing sampling-based methods.

Contribution

The paper presents a novel integrated model that combines multi-task learning with TSP solving for improved multi-goal path planning efficiency and accuracy.

Findings

Superior performance in computation time compared to sampling-based algorithms

Achieves lower solution costs in simulated environments

Effective in complex obstacle scenarios

Abstract

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.