Smooth Path Planning Using a Gaussian Process Regression Map for Mobile Robot Navigation

TL;DR

This paper presents a method for smooth ground robot path planning in hazardous environments by combining Gaussian process regression environment modeling with gradient descent Bézier curve optimization, improving path safety and smoothness.

Contribution

It introduces a novel approach integrating Gaussian process regression with Bézier curve optimization for smooth, safe path planning in unstructured terrains.

Findings

GPR provides a continuous differentiable terrain model.

The combined method yields smoother and safer paths.

Numerical experiments validate the approach's effectiveness.

Abstract

In the context of ground robot navigation in unstructured hazardous environments, the coupling of efficient path planning with an adequate environment representation is a crucial topic in order to guarantee the robot safety while ensuring the accomplishment of its mission. This paper discusses the exploitation of an environment representation obtained via Gaussian process regression (GPR) for smooth path planning using gradient descent B\'ezier curve optimisation (BCO). A continuous differentiable GPR of the terrain traversability and obstacle distance is used to plan paths with a weighted A* discrete planner, a T-RRT sampling-based planner and BCO using A* or T-RRT computed paths as prior. Numerical experiments in procedurally generated 2D environments allowed to compare the paths planned by the described methods and highlight the benefits of the joint use of the GPR continuous representation and the BCO smooth path planning with these different priors.