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.

翻译：本文研究单台水下协同导航辅助（CNA）车辆为间歇性辅助N个智能体而规划路径的问题，旨在最小化平均导航不确定性。CNA与智能体均建模为恒速运动体。智能体沿已知名义轨迹运动，CNA则规划路径以依次拦截这些智能体。导航辅助过程通过标量离散时间卡尔曼滤波器建模。路径规划中，CNA考虑上浮以降低自身导航不确定性。本文提出一种贪婪规划算法，该算法基于最优辅助时间、整体导航不确定性降低量以及通行时间等启发式因子为CNA分配智能体。通过蒙特卡洛实验，在随机生成智能体名义轨迹与初始导航不确定性的条件下，将该方法与最优（穷举）算法进行对比。