Abstract
Cooperative navigation aims at improving positioning accuracy of Autonomous Underwater Vehicles (AUVs). In this paper, a dual leaders cooperative navigation method is proposed based on Cross Entropy (CE) algorithm. Since the trajectories of the slave AUVs are assumed to be predetermined, the Markov Decision Process (MDP) is also integrated in the proposed algorithm to generate optimal trajectories of master AUVs from the perspective of probability. Firstly, the navigation model and cost functions are established for the cooperative navigation system with multiple masters and slaves. Then, the CE algorithm is used to train the system with help of MDP to obtain the path of the master AUVs. In the simulation, the cooperative localization trajectories of the slave AUVs are obtained by Extended Kalman Filter (EKF) and are compared with other positioning methods. The results show that the trajectories of dual master AUVs obtained by the proposed algorithm can not only reduce the observation error of the slave AUVs in the system effectively, but also keep relative measurement distance between the master AUVs and the slave AUVs in a suitable range.
Highlights
Autonomous Underwater Vehicles (AUVs) can expand scope of human marine activities
Extended Kalman Filter (EKF) is applied to cooperative navigation system after trained by Cross Entropy (CE) algorithm to obtain trajectories of slave AUVs
MODEL OF PATH PLANNING According to the above analysis, the purpose of this paper is to find the most effective navigation strategy, so that the adjacent two measurement angles of the master AUVs and the slave AUVs in the cooperative navigation process are as close as possible to 90◦ to improve the overall positioning accuracy
Summary
Autonomous Underwater Vehicles (AUVs) can expand scope of human marine activities. With the improvement of efficiency requirements for underwater operations, the usage of networked Autonomous Underwater Vehicle (AUV) for collaborative work has recently become a hot issue in the field of marine engineering. The operation path of the slave AUVs are planned before tasks It is the master AUVs’ task to maneuver and improve the cooperative navigation positioning accuracy of the slave AUVs. In other words, the master AUVs need to plan optimal path to minimize the observation error of the slave AUVs. Since the Cross Entropy (CE) is suitable for measuring uncertain information [19], it is widely used for optimization problems, such as enterprise resource planning system selection [20] and information retrieval [21], etc. A proper cost function to train the system based on CE algorithm is established to reduce the observation error of the slave AUVs. In addition, EKF is applied to cooperative navigation system after trained by CE algorithm to obtain trajectories of slave AUVs. Morever, the proposed method is compared with other navigation methods to show its superiority. The conclusion is given, and some pending further research directions are proposed In section V
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