2024/07/16 by Yuanjie Lu, Dibyendu Das, Lu, Yuanjie +5 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotics (cs.RO) #Teaching and Learning Programming
paper · pdf · doi:10.48550/arxiv.2407.11399
openalex publication_date 2024/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which result in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique that allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our technique predicts collision-free and dynamically-feasible trajectories and distances between goal pairs to guide the sampling process to build a roadmap, to inform a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.