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

2025-03-19

AI Summary

This work addresses high-difficulty motion planning under sparse experience—only 1–100 demonstrations—in complex, cluttered environments. We propose IERTC*, a novel sampling-based planner that integrates micro-path deformation, heuristic rewiring, and environment-complexity-driven adaptive sampling. Crucially, IERTC* dynamically selects between straight-line and curved trajectories based on local obstacle density, establishing the first “local environmental awareness–trajectory policy switching” planning paradigm. Evaluated on dense-obstacle benchmarks, IERTC* achieves a 49.3% success rate improvement with 100 demonstrations and 43.8% with only one demonstration, relative to baseline methods. Moreover, it reduces path cost by 56.3% and 57.8%, respectively. These results demonstrate a substantial breakthrough in optimal path planning under extreme data scarcity, effectively overcoming the small-sample bottleneck in robotic motion planning.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing 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 re-wiring process and an informed sampling process. The core idea of this algorithm is to apply different strategies depending on the complexity of the local environment; for example, it adopts a more complex curved trajectory if obstacles are densely arranged near the search tree, and it adopts a simpler straight line if the local environment is sparse. The results of experiments using a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in difficult problems in the cluttered environment (an average improvement of 49.3% compared to the state-of-the-art algorithm) while also significantly reducing the solution cost (a reduction of 56.3%) when using 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).

Problem

• High-quality path solution with limited dataset
• Flexible search tree exploration using IERTC* algorithm
• Improved planning success in cluttered environments

Innovation

• IERTC* algorithm generalizes limited dataset efficiently
• Uses re-wiring and informed sampling to reduce path cost
• Adapts strategies based on local environment complexity