Multi-Goal Motion Memory
<|reference_start|>Multi-Goal Motion Memory: 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.<|reference_end|>
@article{lu2024multi-goal,
    title={Multi-Goal Motion Memory},
    author={Yuanjie Lu, Dibyendu Das, Erion Plaku, and Xuesu Xiao},
    journal={arXiv preprint arXiv:2407.11399},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11399},
    primaryClass={cs.RO}
}