Conditional Generative Adversarial Networks for Optimal Path Planning

TL;DR

This paper introduces a novel learning-based path planning method combining conditional GANs with RRT* to efficiently generate optimal collision-free paths in complex environments.

Contribution

It proposes a new CGAN-based model to produce feasible path distributions, enhancing RRT* efficiency for optimal path planning.

Findings

CGAN-RRT* outperforms conventional RRT* in efficiency

The model generates realistic feasible path distributions

Testing shows improved path optimality and speed

Abstract

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of optimal collision-free path are both critical parts for solving path planning problem. Although conventional sampling-based algorithms, such as the rapidly-exploring random tree (RRT) and its improved optimal version (RRT*), have been widely used in path planning problems because of their ability to find a feasible path in even complex environments, they fail to find an optimal path efficiently. To solve this problem and satisfy the two aforementioned requirements, we propose a novel learning-based path planning algorithm which consists of a novel generative model based on the conditional generative adversarial networks (CGAN) and a modified RRT* algorithm (denoted by CGANRRT*). Given the map information, our CGAN model can generate an efficient possibility distribution of feasible paths, which can be utilized by the CGAN-RRT* algorithm to find the optimal path with a non-uniform sampling strategy. The CGAN model is trained by learning from ground truth maps, each of which is generated by putting all the results of executing RRT algorithm 50 times on one raw map. We demonstrate the efficient performance of this CGAN model by testing it on two groups of maps and comparing CGAN-RRT* algorithm with conventional RRT* algorithm.