Guaranteed Rejection-free Sampling Method Using Past Behaviours for Motion Planning of Autonomous Systems
This work addresses motion planning efficiency for autonomous systems like drones and vessels, though it appears incremental as it builds on existing kernel density estimation techniques.
The paper tackles the problem of generating rejection-free samples for motion planning in autonomous systems by introducing a learning-based sampling strategy that uses historical data and kernel density estimation. The method is demonstrated to guarantee rejection-free sampling in 2D and 3D case studies, with statistical validation via Monte Carlo simulations.
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.