Path planning method based on bat optimization algorithm for intelligent robot
In order to solve the problem of low fitness value and long planning time resulting from the failure to obtain the target signal strength for intelligent robot in path planning,an intelligent robot path planning method based on bat optimization algorithm was proposed.The robot model was established to obtain its target signal strength,and the moving target was searched by particle swarm optimization.Bat algorithm and golden sine algorithm were combined to obtain the average position of population.The robot's moving path was planned by searching process in stages.The results show that the path planning time of as-proposed method is only 2.0 s,the fitness is 24.1,and the number of infeasible solutions is zero.The as-proposed method can effectively improve the fitness value with reduced planning time,showing feasibility and value for practical application.
bat optimization algorithmtarget signal strengthintelligent robotpath planningplanning methodgolden sine algorithmparticle swarm optimizationrobot model
万方数据
维普
