基于人工势场的机器人动态路径规划新方法

针对传统机器人路径规划方法仅考虑静态环境的问题,建立了一种基于人工势场的移动机器人动态路径规划新方法.移动机器人运动环境通常是复杂多变的,在动态环境下,目标点、障碍物可能都是运动的,另外,存在运动轨迹未知的随机障碍物等,针对移动环境的动态情况,在传统人工势场法相对位置势场的基础上引入相对速度势场,充分利用量子粒子群算法,对引力势场和斥力势场的增益系数进行一定的优化.以量子粒子群的优化算法进行快速全局搜索,结合人工势场操作,对引力场和斥力场增益系数进行优化,该方法易于实时快速地对机器人进行控制.仿真结果表明,基于量子粒子群算法的人工势场法的路径规划模型能够得到平滑、安全的路径,具有较高的性能.该方法可以有效地实现机器人的动态路径规划.

Dynamic path planning research for mobile robot based on artificial potential field

In order to overcome the disadvantage which the traditional method only consider the static environment, The dynamic path planning method for mobile robot based on artificial potential field is established, the dynamic environment is usually complex. In dynamic environment, the target point, the movement and an obstacle may have been move. There is a trajectory of unknown random obstacles. Based on the traditional artificial potential field relative position on the introduction of potential field relative velocity potential field, taking advantage of the quantum particle swarm algorithm, the gravitational potential field and repulsion potential field can be optimal for dynamic situation. Using quantum particle swarm optimization algorithm for rapid global search, repulsive gravitational field and gain coefficients are optimized in real time combined with artificial potential field of operation, which is easy to control the robot quickly. Simulation results show that safe and smooth path can be obtained based on artificial potential field method, which has higher performance. This method can effectively achieve the dynamic path planning of the robot.