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With the rapid development of society, a wide variety of consumer applications are increasingly emerging. At the same time, the involvement of intelligent technologies such as deep learning, reinforcement learning, and quantum computing is empowering consumer applications by driving them to be smarter, more secure, and digitized. Among them, the underwater field is an important application direction, such as equipment overhaul, scientific research, resource exploration, and so on. This paper targets the detection, optimization, and inference tasks in underwater applications, aiming to design efficient and safe solution algorithms for them using new techniques. First, we establish an underwater mission scenario, using time-varying current data to create a 3D ocean environment model, which can satisfy the requirements of different underwater applications. Second, a safe and efficient underwater object detection algorithm is designed, which constructs a deep neural network to extract valid information from redundant environments. Finally, a path planning algorithm for underwater unmanned equipment clusters is developed to solve the optimization decision problem through deep reasoning computation. We carry out a series of comparative experiments, which adequately prove that the algorithm proposed in this paper has good superiority, can cope with the interference of different intensities of ocean currents, and ensures the operational effect of the cluster formation.
Xi et al. (Thu,) studied this question.