Mobile robot path planning based on improved dung beetle optimization algorithm

To make the mobile robot autonomously plan a collision-free shortest path in an unknown complex environment, a dung beetle optimization algorithm in the swarm intelligence algorithm is applied to the path planning problem. The basic dung beetle optimization technique is not very good at global exploration. Therefore, this paper improves it in two aspects. The first step in initializing the population is to employ an enhanced Sine chaotic mapping, which has a more uniform distribution in phase space, instead of random numbers. Secondly, dung beetles' dancing locations are updated by using the MSA algorithm. The DBO method's global optimization capacity and convergence accuracy are improved by the nonlinear decreasing control parameters and adaptively varying inertia weight coefficients in the MSA algorithm. Through the creation of a grid map simulation environment in Matlab, it is possible to observe that the enhanced dung beetle optimization algorithm performs better in finding the shortest path than both the DBO algorithm and the GWO algorithm by converging faster and searching for shorter path lengths.