Thomas T. Enevoldsen

Thomas T. Enevoldsen, Roberto Galeazzi

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.

Thomas T. Enevoldsen, Roberto Galeazzi

The paper presents a path planning algorithm based on RRT* that addresses the risk of grounding during evasive manoeuvres to avoid collision. The planner achieves this objective by integrating a collective navigation experience with the systematic use of water depth information from the electronic navigational chart. Multivariate kernel density estimation is applied to historical AIS data to generate a probabilistic model describing seafarer's best practices while sailing in confined waters. This knowledge is then encoded into the RRT* cost function to penalize path deviations that would lead own ship to sail in shallow waters. Depth contours satisfying the own ship draught define the actual navigable area, and triangulation of this non-convex region is adopted to enable uniform sampling. This ensures the optimal path deviation.