Obstacle Avoidance Problem Study Of Robot Based On Artificial Potential Field Method

Robot obstacle avoidance technology and algorithm optimization is an important branch and research focus of intelligent robot technology.The obstacle avoidance algorithm is divided into two categories: global obstacle avoidance algorithm and local obstacle avoidance algorithm.In this paper,the artificial potential field method of the local obstacle avoidance algorithm,which has strong adaptability to the unknown environment,is convenient for modeling and less computation,has been analyzed and studied emphatically.The traditional artificial potential field method has two kinds of defects,respectively,the target unreachable problem and the two type of local minimum problem,the first type of local minimum problem is the robot,the obstacle,and the target point are in a straight line,and the obstacle is located between the robot and the target point,in the second type of local minimum problem,the ligature between the robot and the target point has no the influence range of obstacles which includes the robot,and the second type of local minimum problem is also the commonest type of local minimum problem.In order to solve the problem of unreachable goals,an modified repulsive field function artificial potential field method is proposed,the traditional artificial potential field method repulsive force field function is multiplied by the N order of the distance between the robot and the target point,it guarantees that the target point is the minimum point of the global potential field.After analyzing the modified repulsive field function artificial potential field method is only suitable for solving the problem of unreachable target,but is not suitable for solving the problem of local minimum,this paper designs the “escape force” idea to optimize the modified repulsive field function artificial potential field method,when the robot falls into the local minimum problem,the escape force optimization idea is used to provide an additional control force for the robot,helping the robot escape from second types of local minimum points,the robot can pass directly through the two obstacles,the problem of unreachable target and second kinds of local minimum problems are solved at the same time.Because the distribution of obstacles in the unknown environment is random and probably very dense,there are likely to be both target unreachable problems and two types of local minimum problems.In order to solve the target unreachable problem and the two types of local minimum problem at the same time,an combinatorial optimization artificial potential field method is proposed,combining the modified the repulsive field function artificial potential field method with redefining the direction of the repulsive force component optimization idea,promising the included angle of robot attraction direction given by the target point and the robot repulsive force component direction given by each obstacle is not more than 90 degrees,eliminating the necessary condition conditions of target unreachable problem and two types of local minimum problems producing,not only the two types of local minimum problem and the target unreachable problem are solved simultaneously,but also the original advantages of the artificial potential field method are retained,the simulation results show that the algorithm is very suitable to the environment with obstacles being large in number,dense in distribution,and random in position,the planned path is very reasonable,making up for all the limitations of the modified repulsive field function artificial potential field method and the modified repulsive field function artificial potential field method join with escape force optimization idea.