Colony Path Planning Based on Robot Ant Algorithm with Multi-Factor Optimization
In the path planning using traditional ant colony algorithm,there are problems such as slow convergence speed,unsmooth path,and poor directionality and purpose.Therefore,a multi-factor optimization ant col-ony algorithm was proposed to improve the performance of path optimization.The diffusion method was used to endow the map with uneven initial pheromone,which can provide better directionality for path search and avoids the appear-ance of local optimal solutions.Distance heuristic information,obstacle resistance heuristic information and path angle heuristic information were utilized as comprehensive heuristic information,to enhance the purpose of ant movement and shorten the length of the path.The Bezier curve was used to optimize the inflection point on the path and output a smooth path,which provides a method basis for the efficient operation of the robot.The simulation results show that the multi-factor optimization ant colony algorithm has fast convergence speed,good stability,and the optimal path is shorter and smoother,which is more meaningful for engineering practice.
Ant colony algorithmPath planningDiffusionComprehensive enlightenment informationBezier curve
NETL
NSTL
万方数据
