Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Intermittently
CoRR(2024)
摘要
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.
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