3-D Path Planning for AUVs Based on Improved Exponential Distribution Optimizer

Autonomous underwater vehicles (AUVs) have become an important technology in the field of the Internet of Underwater Things (IoUT). However, the complexity and unknown nature of the underwater environment poses a great challenge to the autonomous operation of AUVs. An efficient and stable path-planning algorithm is the key for AUVs to achieve autonomous operation. To address the above problems, this article proposes an improved exponential distribution optimizer (IEDO) for 3-D path planning. In the proposed algorithm, population initialization, the optimization algorithm itself, and the local optimum problem are all addressed and improved. The algorithm population is first initialized using an oriented initialization method that obeys a Gaussian distribution to improve the efficiency of the algorithm in the early stages. Second, for the IEDO algorithm itself, the convergence rate of the algorithm is further improved by adding the guided solution generated by its iterative process to the iterative selection of the population. Finally, the crossover-mutation idea of the genetic algorithm is integrated to improve the global search ability of the population and avoid falling into the local optimum problem. In terms of algorithm validation, two real seabed terrain data sets are used for a simulation verification of the algorithm and compared with the existing algorithms. The results prove that the IEDO algorithm proposed in this article has a strong convergence speed, strong global search capability, and good path qualities.