In the unknown environment without prior conditions, the path planning and navigation of an underwater autonomous underwater vehicle (AUV) is a big challenge. This study presents a perceptive navigation approach without a prior geomagnetic map. It achieves efficient path planning and geomagnetic map creation by combining deep reinforcement learning (DRL) with a simulated annealing (SA) algorithm. A deep Q network (DQN) is built to explore the environment of the carrier, collect local geomagnetic data, and then use the collected data to train a regression model to predict the global geomagnetic map. At the same time, the simulated annealing algorithm is used to optimize the path of an underwater AUV to avoid the local minimum problem of carrier space search. The success of the suggested approach is confirmed through a number of simulated tests in terms of path length, exploration efficiency, and geomagnetic map correctness. The results show that this method can significantly improve the navigation performance of underwater AUV in an unknown environment, and provide a new way for the construction of geomagnetic maps.

In both military defense and ocean exploration, autonomous underwater vehicle (AUV) are crucial. To address the navigation issue of underwater autonomous unmanned submersibles in the absence of prior information, a geo-sensory navigation orientation method based on circular arc path search is proposed. By initially creating a geo-sensory navigation model for the car, this technique turns navigation into a multi-objective optimization problem. Secondly, the vehicle follows a circular arc path and perceives the variation trend of the objective function in the path space to determine the target direction. Furthermore, the method achieves the navigation orientation job of autonomous unmanned submersibles by adaptively choosing alternative search intervals to accommodate varied search stages. It is shown through simulated studies that this approach can make it possible for underwater autonomous unmanned submersibles to arrive at the predefined destination area without the need for previous magnetic field maps. By comparing with other search algorithms, the validity and efficiency of the proposed algorithm are validated.