引入模糊斥力的改进人工势场法及机器人局部路径规划
Local Path Planning Based on Improved Artificial Potential Field Using Fuzzy Repulsion Force for Robot
DOI: 10.12677/JEE.2014.23007, PDF,  被引量    科研立项经费支持
作者: 吴晓光, 龚思沛, 谢 平：燕山大学电气工程学院，河北省测试计量技术及仪器重点实验室，秦皇岛
关键词: 机器人；局部路径规划；人工势场；局部最小；模糊控制；Mobile Robot； Local Path Planning； Artificial Potential Field； Local Minimal Point； Fuzzy Control
摘要: 针对传统人工势场法中存在的局部最小问题，提出一种引入模糊斥力的改进人工势场法。该算法首先在改进传统人工势场法的引力场和斥力场函数的基础上，通过确定引力场与斥力场增益的合理取值范围，解决目标位置靠近障碍物造成的目标位置不可达问题(GNRON)。接着利用模糊控制算法计算模糊斥力，并将其引入到改进人工势场法的机器人虚拟合力计算中，使得机器人在目标位置与起始位置间存在不同障碍物时仍能快速完成局部路径规划任务，进一步解决局部最小问题。仿真与室内模拟实验表明，运用该算法能够有效的解决局部最小问题。
Abstract: An improved artificial potential field method is proposed to solve the local minimum which exists in the traditional artificial potential field. First, on the basis of improving the attractive field and repulsive field functions of traditional artificial potential field, the problem on goals non-reacha- ble with obstacles nearby (GNRON) is solved by determining the reasonable range of gain of gra-vitational field and repulsive field. Then, the fuzzy control algorithm is used to calculate the fuzzy repulsion force and the situation when different obstacles are located between the robot and the goal is solved by calculating the virtual force using the fuzzy repulsion force which navigates the robot moving around the obstacle. At last, the proposed approach is verified by MATLAB simulation and indoor experiment, the results of which illustrate that the improved artificial potential field method is effective to solve the local minimal problem.
文章引用：吴晓光, 龚思沛, 谢平. 引入模糊斥力的改进人工势场法及机器人局部路径规划[J]. 电气工程, 2014, 2(3): 48-60. https://dx.doi.org/10.12677/JEE.2014.23007

参考文献

[1] 林凯 (2010) 未知环境下移动机器人路径规划. 硕士学位论文,湖南师范大学, 长沙.
[2] Qin, Z., Bai, M. and Ralescu, D. (2011) A fuzzy control system with application to production planning problems. Information Sciences, 181, 1018-1027.
[3] 康亮, 赵春霞, 郭剑辉, 等 (2009) 未知环境下改进的基于RRT算法的移动机器人路径规划. 模式识别与人工智能, 3, 337-343.
[4] 吴晨光 (2012) 基于改进人工势场法的机器人路径规划及其在RoboCup中的应用. 硕士学位论文, 南京邮电大学, 南京.
[5] 孙俊 (2009) 移动机器人路径规划. 硕士学位论文, 杭州电子科技大学, 杭州.
[6] 李庆中, 顾伟康, 叶秀清, 等 (2002) 基于遗传算法的移动机器人动态避障路径规划方法. 模式识别与人工智能, 2, 161-166.
[7] 卢恩超, 张邓斓, 宁雅男, 等 (2012) 改进人工势场法的机器人路径规划. 西北大学学报(自然科学版), 5, 735- 738.
[8] 孟蕊,苏维均,连晓峰, 等 (2010) 基于动态模糊人工势场法的移动机器人路径规划. 计算机工程与设计, 7, 1558- 1561.
[9] Adeli, H., Tabrizi, M.H.N., Mazloomian, A., et al. (2011) Path planning for mobile robots using iterative artificial potential field method. International Journal of Computer Science Issues, 8, 28-32.
[10] Jia, Q. and Wang, X. (2010) An improved potential field method for path planning. IEEE Control and Decision Conference (CCDC), 2265-2270.
[11] Jia, Q. and Wang, X. (2009) Path planning for mobile robots based on a modified potential model. IEEE International Conference on Mechatronics and Automation, 4946-4951.
[12] Zhang, J.Y. and Liu, T. (2007) Optimized path planning of mobile robot based on artificial potential field. Acta Aeronautica et Astronautica Sinica, 28, 183-188.
[13] 王志娟 (2008) 模糊控制中高级算法的设计和研究. 硕士学位论文, 山东大学, 济南.
[14] Ge, S.S. and Cui, Y.J. (2000) New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation, 16, 615-620.