A Novel Improved Generalized Normal Distribution Optimization Algorithm for Path Planning of AUV in a 3D Environment

Path planning technology is the key to realizing the precise operation of underwater robots. However, due to the complexity of the underwater environment and the limitations of multiple planning conditions, it is difficult for underwater robots to generate reliable and reasonable paths. In response to this challenge, this paper proposes an improved Generalized Normal Distribution Optimization (GDNO) algorithm. Firstly, the circle chaotic mapping strategy is introduced to ensure the uniform distribution of the initialized population. Secondly, the mathematical optimizer acceleration function is utilized to achieve a balance between the exploration and exploitation strategies. Finally, an improved search strategy based on basic addition and subtraction operations and optimal value tracking is put forward. The experimental results show that the improved GDNO exhibits excellent performance in path planning, providing new perspectives and methods for the research on path planning technology and swarm intelligence algorithms.