Intelligent Obstacle Avoidance Method of Building Materials Transportation Robot Based on Ant Colony Potential Field Algorithm
An ant colony potential field algorithm was designed to solve the problems of poor global optimization ability and easy collision with moving obstacles in existing building material transport robots.Firstly,the accumulation process of individual phero-mone concentration under the ant colony algorithm is analyzed,and the cooperation of gravity and repulsion force is solved by con-structing an artificial potential field,which is regarded as an important constraint for selecting the heuristic factor of the ant colony al-gorithm.Secondly,a simulated annealing(SA)algorithm is introduced to optimize the ant colony potential field algorithm twice,and the cooling process is regarded as a global optimization process.Finally,the mass point model is constructed in terms of local collision avoidance,and the penalty function is established by evaluating the robot's current position,running speed and obstacle position,and the penalty function value is reduced to the minimum to avoid collision with obstacles.Experimental results show that the proposed al-gorithm has higher iterative efficiency,and the shortest travel distance of 110.6 m under complex dynamic conditions,while the shor-test travel distance of the four traditional algorithms is 135.5 m,137.6 m,137.2 m and 130.4 m,respectively.Moreover,under the control of the proposed algorithm,there is no local collision with other mobile robots.
ant colony potential fieldtransport robotintelligent obstacle avoidanceheuristic factormass point model
万方数据
维普
