Experience-based Optimal Motion Planning Algorithm for Solving Difficult Planning Problems Using a Limited Dataset

Abstract

This study addresses the challenge of generating high-quality motion plans within a short computation time using only a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing the path cost by introducing a rewiring process and an informed sampling process. Unlike recent learning-based or generative methods that rely on model training or probabilistic priors, IERTC* employs a non-parametric retrieve-and-repair strategy to generalize prior experiences without requiring pretraining or large datasets. This design facilitates broad exploration beyond the original experience, robust adaptation to unseen environments, high flexibility in cluttered environments, and efficient deployment without offline training. Experimental results from a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in the cluttered environment compared to a state-of-the-art optimal motion planning algorithm (an average improvement of 49.3%) while also comparable reduction of the solution cost (a reduction of 56.3% from a benchmark algorithm) utilizing just one hundred experiences. Furthermore, the results demonstrated outstanding planning performance even when only one experience was available (a 43.8% improvement in success rate and a 57.8% reduction in solution cost).