A novel path planning method of mobile robots based on an improved bat algorithm

In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.

Automated summary

This paper presents an improved bat algorithm based on inertial weight and Levy flight to solve the global path planning problem of mobile robots. The algorithm utilizes linear inertial weights, Levy flight, and Cauchy Distribution to prevent premature convergence and enhance local mining ability.