摘要
针对传统人工势场法应用于移动机器人路径规划存在的缺陷,建立了改进的人工势场模型:使用势场强度代替力矢量进行路径规划;在障碍物的斥力势场中添加系数项,解决障碍物与目标点过近导致的目标不可达问题;考虑移动障碍物速度与机器人速度的影响,将速度信息引入到势场函数中;引入"填平势场"引导机器人走出局部极小点.在改进人工势场模型基础上,将各种势场强度用代数和方式叠加,用遗传信赖域算法搜索机器人在一个采样周期中移动范围内的势场强度之和最小的点,多个最小点构成全局优化路径.实验结果表明,该方法能够较好地实现动态环境下移动机器人的路径规划.
To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved method was proposed, in which potential field intensity was used instead of force vector to plan the path for the mobile robot. By addition of coefficient item to repulsion potential field of obstacles, the destination unreachable problem caused by the closely distance between destination and obstacles was solved. Considering the speed-effect of mobile obstacles and mobile robot, the velocity information was introduced into potential field function and an ＂added potential field＂ was also introduced to guide the robot to be out of local minimum points. Based on the new method, all the potential field intensity was added by algebraic sum style, then the genetic trust regidn algorithm was used to search the minimum sum point of potential field intensity within the movement scope which the robot can arrive at during a sampling period, and the global optimization path was composed of all the minimum points. Experiment results show that better path planning for mobile robot in dynamic environment can be achieved by this new method.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2011年第1期50-55,共6页
Journal of Harbin Institute of Technology
基金
高技术研究发展计划资助项目(2006AA04Z245)
国家重大科技专项项目(2009ZX04004-062)
关键词
移动机器人
人工势场
遗传信赖域
路径规划
mobile robotics
potential field
genetic trust region
path planning
