Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Intermittently

This paper considers the problem of planning a path for a single underwater cooperative navigation aid (CNA) vehicle to intermittently aid a set of N agents to minimize average navigation uncertainty. Both the CNA and agents are modeled as constant-velocity vehicles. The agents traverse 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 based on an optimal time-to-aid, overall navigation uncertainty reduction, and transit time, to assign agents to the CNA. The approach is compared to an optimal (exhaustive enumeration) algorithm through a Monte Carlo experiment with randomized agent nominal trajectories and initial navigation uncertainty.