足球机器人路径规划的改进型人工势场算法研究

传统人工势场法不能适应复杂动态环境且容易产生局部极小,论文提出了一种改进型的人工势场算法,该算法考虑了机器人和障碍物的速度、加速度等动态特性,对传统人工势场进行了有效的调节,使其能更好地适应动态复杂环境,对局部极小问题进行判定,通过改变斥力场和引力场的影响力来解决局部极小问题,将该优化算法运用到足球机器人仿真比赛中,结果表明基于改进型人工势场优化算法能够在动态对抗性的环境中有效地实现最优路径规划,弥补了传统人工势场的不足。

Study on Evolutionary Artificial Potential Field Based Path Planning in Robocup

Traditional APF(Artificial Potential Field) can not adapt to complex and dynamic environment effectively and brings local minimum problem easily,an evolutionary Artificial Potential Field algorithm is proposed,in which the velocity and acceleration of obstacles and Robots are considered and the traditional Artificial Potential Field is adjusted,which can adapt to dynamic environment effectively,while the local minimum problem is determined and solved by modifying the affect force of repulsive potential and attractive potential.This evolutionary algorithm is applied to path planning of RoboCup,simulation results indicate that evolutionary Artificial Potential Field can realize optimal path planning in dynamic and oppositional environment effectively,which makes up the disadvantages of traditional Artificial Potential Field.