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机器人技术是当今国际上研究的热点项目之一,而路径规划则是机器人研究的一个极为重要的领域之一,也是促使机器人能在各种复杂环境中精准完成任务的必要基础。人工势场法是Khatib提出的一种虚拟力法,该算法结构简单,便于低层的实时控制,在移动机器人实时避障和平滑的轨迹控制方面,得到了广泛应用。为了解决“机器人在到达目标位置前由于陷入局部极小值而无法到达目标位置”的问题,本文提出了改进算法,该算法能够有效解决经典人工势能场法的局部极值问题,协助移动机器人逃脱极值点达到目标位置。通过在Matlab仿真平台上进行仿真实验,证明了该方法的有效性和适用性。
Robotics is one of the most popular research projects in the world at present. Path planning is one of the most important fields in robotics research. It is also the necessary basis for robotics to accomplish the tasks accurately in various complex environments. The artificial potential field method is a kind of virtual force method proposed by Khatib. The proposed algorithm has the advantages of simple structure, easy real-time control in low level and wide application in real-time obstacle avoidance and smooth trajectory control of mobile robots. In order to solve the problem that the robot can not reach the target position because of falling into a local minimum before it reaches the target position, this paper proposes an improved algorithm which can effectively solve the local extremum problem of the classical artificial potential field method and assists the mobile The robot reaches the target position after reaching the extreme point. The simulation experiment on Matlab simulation platform proves the validity and applicability of this method.