Improvement Of Artificial Potential Field Algorithm For Robots In Different Environments

With the rapid development of robotic technologies,robots can be seen everywhere in human's life and work.No matter what type of robots they are of,moving is a fundamental behavior of theirs.With more and more unknown fields had been explored,the path planning ability of mobile robots is becoming more and more important.Therefore,it is more and more important to study the ability of mobile robot path planning in different working environments.This thesis studies the path planning technology of mobile robot in different environments by optimizing the artificial potential field method.The thesis focuses on the following aspects:(1)Analyzing the path planning principle of artificial potential field method to solve the common problem of ‘goal nonreachable with obstacle nearby' in artificial potential field method.By modifying the gravitational field function,the robot can finally reach the target point on the condition that the gravitation on mobile robot is larger than the repulsion force from the repulsion field when it approaches the target point.(2)Expanding obstacles which cannot be regarded as particles into a circle on a twodimensional map to make up the deficiency in the traditional artificial potential field method that obstacles can only be regarded as static particles.According to the size and speed of the expanding circle of the moving obstacle,the relative velocity can be analyzed and judged while doing path planning.While planning obstacle avoidance measures,it is taken into account that the mobile robot has its minimum turning radius in the planned avoidance routes.Making the planned route more optimized by combining the artificial potential field method and the Dubins curve.(3)Combining the artificial potential field method and the A-star algorithm.Artificial potential field method is used as map-wide path planning while A-star algorithm avoidance algorithm to avoid large obstacles.Firstly,the discriminant factor is added to the artificial potential field method function to determine when to carry out the A-star algorithm.Then two closed lists are set up in the A-star algorithm to judge when to terminate the A-star algorithm.The algorithm ensures the security of the planning path and reduces the planning cost.