Guaranteed Rejection-free Sampling Method Using Past Behaviours for Motion Planning of Autonomous Systems

TL;DR

This paper introduces a learning-based sampling method for motion planning that guarantees rejection-free samples by leveraging past system data and kernel density estimation, demonstrated on real autonomous systems.

Contribution

It proposes a novel rejection-free sampling strategy using historical data and kernel densities, ensuring samples stay within the free space in motion planning.

Findings

Successfully applied to autonomous vessel and drone case studies.

Guarantees rejection-free sampling in the planning process.

Statistically validated through Monte Carlo simulations.

Abstract

The paper presents a novel learning-based sampling strategy that guarantees rejection-free sampling of the free space under both biased and approximately uniform conditions, leveraging multivariate kernel densities. Historical data from a given autonomous system is leveraged to estimate a non-parametric probabilistic description of the domain, which also describes the free space where feasible solutions of the motion planning problem are likely to be found. The tuning parameters of the kernel density estimator, the bandwidth and the kernel, are used to alter the description of the free space so that no samples can fall outside the originally defined space.The proposed method is demonstrated in two real-life case studies: An autonomous surface vessel (2D) and an autonomous drone (3D). Two planning problems are solved, showing that the proposed approximately uniform sampling scheme is capable of guaranteeing rejection-free samples of the considered workspace. Furthermore, the effectiveness of the proposed method is statistically validated using Monte Carlo simulations.