Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially
<|reference_start|>Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially: This paper considers planning a path for a single underwater cooperative navigation aid (CNA) vehicle to sequentially aid a set of N agents to minimize average navigation uncertainty. Both the CNA and agents are modeled as constant-velocity vehicles. The agents travel along known nominal trajectories and the CNA plans a path to sequentially intercept them. Navigation aiding is modeled by a scalar discrete time Kalman filter. During path planning, the CNA considers surfacing to reduce its own navigation uncertainty. A greedy planning algorithm is proposed that uses a heuristic to schedule agents to the CNA that is based on the optimal time-to-aid, the overall navigation uncertainty reduction, and the transit time. The approach is compared to an optimal (exhaustive enumeration) algorithm through a Monte Carlo experiment with randomized agent trajectories and initial navigation uncertainty.<|reference_end|>
@article{wolek2024path,
    title={Path Planning for a Cooperative Navigation Aid Vehicle to Assist
  Multiple Agents Sequentially},
    author={Artur Wolek},
    journal={arXiv preprint arXiv:2402.17071},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17071},
    primaryClass={eess.SY cs.RO cs.SY}
}