3D Path Planning Algorithm for Unmanned Underwater Vehicles Based on Improved Grey Wolf Optimization Algorithm

To enhance the route planning capabilities of underwater unmanned vehicles in complex three-dimensional ocean environments and to overcome the limitations of traditional planning methods, such as simplistic handling of environmental factors and optimization indicators, this paper introduces a new three-dimensional route planning algorithm from the perspectives of navigation safety and stealth to achieve optimal route planning. Firstly, addressing the issues of insufficient global search capability, low optimization accuracy, and slow convergence speed in traditional grey wolf optimization algorithm, this paper proposes an improved grey wolf optimization algorithm. This algorithm implements a nonlinear adjustment of the convergence factor, thereby better balancing the global and local search capabilities of the algorithm. Additionally, it introduces a speed evolution factor into the algorithm, enabling adaptive mutation within the grey wolf population and enhancing the search capability of the algorithm. Secondly, this paper employs a grid model to describe the three-dimensional ocean space environment, taking into account the performance of underwater platforms, terrain, ocean environment, and mission constraints. A three-dimensional route planning strategy based on the improved grey wolf optimization algorithm is constructed, and multi-constraint heuristic functions and multi-indicator evaluation functions are designed. The simulation results demonstrate that the algorithm fully considers the impact of ocean environmental factors and is capable of realizing three-dimensional planning under different performance indicators and mission constraints, effectively meeting the practical navigation needs of underwater platforms.