An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

Abstract

Autonomous sailing robots are a new type of green ship that use wind energy to maintain continuous cruising operations. Compared with traditional algorithms, swarm intelligence optimization algorithms have better intelligence and adaptation. An intelligent algorithm acts as one of the most important solutions to the path planning problem of autonomous sailing robots. The beetle swarm optimization, which is a novel intelligent method that combines the search mechanism of a single beetle with the particle swarm optimization algorithm, is utilized to obtain the optimal path. In this study, the track navigation control of an improved mathematical model of a sailing ship is introduced, and the navigation is tested using a downsized prototype of an autonomous sailing robot. The improved beetle swarm optimization is proposed here by dynamically changing the step size factor and the inertia weight formula. In the iteration of the improved beetle swarm optimization algorithm, the location update cooperates with the beetle monomer search mechanism to learn the update strategy of the particle swarm optimization algorithm. Combinatorial strategies can speed up the overall iterative convergence speed and reduce the possibility that the population will fall into a locally optimal solution. The simulation results demonstrate the robustness, efficiency, and feasibility of the improved beetle swarm optimization in different cases. The research results can provide some references and ideas for the autonomous intelligent navigation control design of autonomous sailing robots.