Multi-Strategy Collaborative Improvement of an H5N1 Viral-Inspired Optimization Algorithm for Mobile Robot Path Planning

Mobile robots play an important role in promoting industrial intelligence and modernization. However, the existing obstacle avoidance path planning algorithms for mobile robots have poor stability and applicability. To this end, this paper proposes a path planning scheme for mobile robots based on ISH5N1 algorithm. Firstly, aiming at the problem of low initial population quality of SH5N1 algorithm, Tent chaos initialization strategy was proposed, which increased the diversity of the population, improved the quality of initial solution, and laid a foundation for subsequent deeper search. Secondly, by fusing the multi-source direction vectors and applying them to the position update, the solution accuracy of the algorithm was improved and the convergence speed of the algorithm was accelerated. Then, the mutation step size control strategy enhanced by Logistic chaos was used to enhance the ability of the algorithm to jump out of local optimum. Finally, the attenuation coefficient of inertia weight is optimized by combining cosine annealing strategy, which strengthens the ability of the algorithm to balance global search and local development. The ISH5N1 algorithm was compared with several commonly used intelligent optimization algorithms on benchmark functions and grid maps with different complexities. The results show that ISH5N1 algorithm shows good stability, higher solution accuracy and faster convergence speed in solving most benchmark functions. In the path planning experiment, the ISH5N1 algorithm can plan a shorter and smoother path, which further proves that the algorithm has good optimization ability and robustness. Finally, ablation experiments were carried out on a 20 × 20 grid map to verify the effectiveness of each optimization strategy.