Optimization of the path planning model through integration of improved RRT* algorithm and ACO algorithm

Path planning is an important part of AUV (Autonomous Underwater Vehicle) exploration system. The path planning of AUV should consider the optimization goal of the path, the path environment, the terrain and obstacles, and the performance constraints of the robot itself. This path planning problem can be solved by various algorithms, but the commonly used algorithms often have many problems. Therefore, aiming at the problems of long sampling time, large amount of calculation and too random path planning results of RRT ^* algorithm, a multi-strategy improved NTDRRT ^* algorithm is proposed. Aiming at the problem that ACO algorithm is easy to fall into local optimum and slow convergence speed, a multi-strategy improved HPACO algorithm is proposed. The NTDRRT ^* algorithm and the HPACO algorithm are combined to form a hybrid improved algorithm, and an AUV path planning model is proposed based on this. Through experimental verification, the hybrid path planning algorithm proposed in this paper has achieved good results in search efficiency, global convergence and solution quality. The research content and results of this paper have certain reference value for improving the efficiency and accuracy of AUV path planning, and have made certain contributions to the development of AUV technology, and can also provide reference and inspiration for research in related fields.