Robot Path Planning Based on Improved Salp Swarm Algorithm

To find an improved method to solve the robot path planning problem, a parasitic salp swarm algorithm based on differential evolution strategy is proposed. The position of salp of the previous generation is added into the leader position update formula to enhance the adequacy of global search. At the same time, the inertia weight of nonlinear decreasing trend is introduced to reasonably adjust the balance between the breadth search and the depth mining of salp leaders in different iteration periods, to improve solution accuracy. The following are introduced into the evolutionary structure: 1) The parasitic and host double populations with different evolutionary mechanisms, 2) their parasitic behaviors, and 3) the idea of survival of the fittest, to increase the diversity of the population and improve the ability of the algorithm to jump out of the local extreme value. The theoretical analysis proves that the time complexity of the improved algorithm is the same as that of the basic algorithm, and the simulation experiment is conducted on 10 standard test functions with different characteristics through six representative comparison algorithms. The results show that the optimization accuracy and stability of the algorithm are significantly improved. Finally, the algorithm is combined with the hermite interpolation method to define the algorithm encoding mode based on the path node. The fitness function and constraint conditions are constructed to bypass obstacles and the shortest paths, to solve the robot path planning problem. Experimental results in different complex obstacle scenes and different interpolation methods for robot path planning show that the improved algorithm is superior to the other five comparison algorithms in terms of the best value, the average value and the variance of the solution results, which also proves the superiority and effectiveness of the fusion Hermite interpolation method in solving the robot path planning problem.