A Complete Algorithm for a Moving Target Traveling Salesman Problem with Obstacles
<|reference_start|>A Complete Algorithm for a Moving Target Traveling Salesman Problem with Obstacles: The moving target traveling salesman problem with obstacles (MT-TSP-O) is a generalization of the traveling salesman problem (TSP) where, as its name suggests, the targets are moving. A solution to the MT-TSP-O is a trajectory that visits each moving target during a certain time window(s), and this trajectory avoids stationary obstacles. We assume each target moves at a constant velocity during each of its time windows. The agent has a speed limit, and this speed limit is no smaller than any target's speed. This paper presents the first complete algorithm for finding feasible solutions to the MT-TSP-O. Our algorithm builds a tree where the nodes are agent trajectories intercepting a unique sequence of targets within a unique sequence of time windows. We generate each of a parent node's children by extending the parent's trajectory to intercept one additional target, each child corresponding to a different choice of target and time window. This extension consists of planning a trajectory from the parent trajectory's final point in space-time to a moving target. To solve this point-to-moving-target subproblem, we define a novel generalization of a visibility graph called a moving target visibility graph (MTVG). Our overall algorithm is called MTVG-TSP. To validate MTVG-TSP, we test it on 570 instances with up to 30 targets. We implement a baseline method that samples trajectories of targets into points, based on prior work on special cases of the MT-TSP-O. MTVG-TSP finds feasible solutions in all cases where the baseline does, and when the sum of the targets' time window lengths enters a critical range, MTVG-TSP finds a feasible solution with up to 38 times less computation time.<|reference_end|>
@article{bhat2024a,
    title={A Complete Algorithm for a Moving Target Traveling Salesman Problem with
  Obstacles},
    author={Anoop Bhat, Geordan Gutow, Bhaskar Vundurthy, Zhongqiang Ren,
  Sivakumar Rathinam, Howie Choset},
    journal={arXiv preprint arXiv:2409.09852},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09852},
    primaryClass={cs.RO}
}