Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913421585285120 |
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| author | Wolek, Artur |
| author_facet | Wolek, Artur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially Wolek, Artur Systems and Control Robotics 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. |
| title | Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2402.17071 |