基于优化遗传算法的移动机器人路径规划
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摘要
针对移动机器人在路径规划过程中,由于传统遗传算法中适应度函数把路径最短作为遗传到下一代主要因素,造成机器人转弯次数过多引起时间浪费问题,提出一种基于改进遗传算法的路径规划方法,通过对适应度函数添加转弯角度控制因子,把路径最短和转弯角度作为路径个体适应度函数值大小的影响因素,并对改进后的适应度函数进行了收敛性分析。最后通过MATLAB进行了仿真分析,结果表明:机器人运动轨迹更加平滑,减少了转弯次数,仿真结果说明该算法具有一定的有效性。
Aiming at the path planning of mobile robots, because the fitness function in the traditional genetic algorithm takes the shortest path as the main factor to be inherited to the next generation,too many times of robot turning lead to the problem of time waste. A path planning method based on improved genetic algorithm was proposed.By adding the control factor of turning angle to the fitness function, the shortest path and the turning angle were taken as the influencing factors of the value of the individual fitness function of the path, and the improved fitness function was analyzed for its convergence. Finally, the simulation experiments were carried out by MATLAB.The results show that the trajectory of robot is more smooth,and the number of turns has been reduced. The simulation results verify the effectiveness of the algorithm.
Aiming at the path planning of mobile robots, because the fitness function in the traditional genetic algorithm takes the shortest path as the main factor to be inherited to the next generation,too many times of robot turning lead to the problem of time waste. A path planning method based on improved genetic algorithm was proposed.By adding the control factor of turning angle to the fitness function, the shortest path and the turning angle were taken as the influencing factors of the value of the individual fitness function of the path, and the improved fitness function was analyzed for its convergence. Finally, the simulation experiments were carried out by MATLAB.The results show that the trajectory of robot is more smooth,and the number of turns has been reduced. The simulation results verify the effectiveness of the algorithm.
引文
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[17] 翟红生,王佳欣.基于人工势场的机器人动态路径规划新方法[J].重庆邮电大学学报(自然科学版),2015,27(6):814-818. ZHAI H S,WANG J X.Dynamic Path Planning Research for Mobile Robot Based on Artificial Potential Field[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2015,27(6):814-818.
[18] 陈敏,谭思云,黄玉清.遗传算法在PID参数整定中的应用[J].仪表技术,2010(5):49-52.CHEN M,TAN S Y,HUANG Y Q.Application of Genetic Algorithm in the PID Parameter Optimization[J].Instrumentation Technology,2010(5):49-52.
[2] HE Q,HU X T,REN H,et al.A Novel Artificial Fish Swarm Algorithm for Solving Large-scale Reliability Redundancy Application Problem[J].ISA Transactions,2015,59:105-13.
[3] 陈曦,谭冠政,江斌.基于免疫遗传算法的移动机器人实时最优路径规划[J].中南大学学报(自然科学版),2008(3):577-583.CHEN X,TAN G Z,JIANG B.Real-time Optimal Path Planning for Mobile Robots Based on Immune Genetic Algorithm[J].Journal of Central South University(Science and Technology),2008(3):577-583.
[4] 吴佳丽.改进遗传算法的机器人路径规划研究[D].西安:西北大学,2012.
[5] 崔建军.基于遗传算法的移动机器人路径规划研究[D].西安:西安科技大学,2010.
[6] FANG N Z,JIAN Z,ZHANG R.A Hybrid of Real Coded Genetic Algorithm and Artificial Fish Swarm Algorithm for Short Term Optimal Hydrothermal Scheduling[J].International Journal of Electric Power & Energy Systems,2014,25(6):617-629.
[7] 申晓宁,郭毓,陈庆伟,等.多目标遗传算法在机器人路径规划中的应用[J].南京理工大学学报,2006,30(6):659-663.SHEN X N,GUO Y,CHEN Q W,et al.Application of Multi-objective Optimization Genetic Algorithm to Robot Path Planning[J].Journal of Nanjing University of Science and Technology,2006,30(6):659-663.
[8] GONG J S,YU X F,XU J M,et al.CT Imaging of a Primary Malignant Mixed Mullerian Tumor Arising from the Peritoneum[J].Clinical Imaging,2008,32(5):390-392.
[9] LIU H S,LAI X B,WU W X.Time-optimal and Jerk-continuous Trajectory Planning for Robot Manipulators with Kinematic Constraints[J].Robotics and Computer Integrated Manufacturing,2013,29(2):309-317.
[10] ARUMUGAM R,ENTI V R,LIU B B,et al.DAvinCi:A Cloud Computing Framework for Service Robots[C]//Proceedings of IEEE International Conference on Robotics and Automation,2010:3084-3089.
[11] MIAO H,TIAN Y C.Robot Path Planning in Dynamic Environments Using a Simulated Annealing Based Approach[C]//Proceedings of 10th International Conference on Control,Automation,Robotics and Vi-sion.Hanoi,Vietnam:[s.n.],2008:1253-1258.
[12] 李擎,冯金玲,柳延领,等.自适应遗传算法在移动机器人路径规划中的应用[J].北京科技大学学报,2008,30(3):316-323.LI Q,FENG J L,LIU Y L,et al.Application of Adaptive Genetic Algorithm to Optimum Path Planning of Mobile Robots[J].Journal Ournal of Science and Technology Beijing,2008,30(3):316-323.
[13] TOMASSINI M.Spatially Structured Evolutionary Algorithms:Artificial Evolution in Space and Time[M].Hei-delberg,Berlin:Springer-Verlag,2005.
[14] 邓志燕,陈炽坤.基于改进遗传算法的移动机器人路径规划研究[J].机械设计与制造,2010(7):147-149.DENG Z Y,CHEN C K.Mobile Robot Path Planning Based on Improved Genetic Algorithm[J].Machinery Design & Manufacture,2010(7):147-149.
[15] THAKUR R S,JAIN R C,PARDASANI K R.Fast Algorithm for Mining Multi-level Association Rules in Large Databases[J].Asian Journal of Information Management,2007,1(1):19-26.
[16] 张海英,范进桢.移动机器人路径规划研究现状及展望[J].微型机与应用,2011,30(2):5-8.ZHANG H Y,FAN J Z.Research Progress and Future Development of Mobile Robot Path Planning[J].Microcomputer & ITS Applications,2011,30(2):5-8.
[17] 翟红生,王佳欣.基于人工势场的机器人动态路径规划新方法[J].重庆邮电大学学报(自然科学版),2015,27(6):814-818. ZHAI H S,WANG J X.Dynamic Path Planning Research for Mobile Robot Based on Artificial Potential Field[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2015,27(6):814-818.
[18] 陈敏,谭思云,黄玉清.遗传算法在PID参数整定中的应用[J].仪表技术,2010(5):49-52.CHEN M,TAN S Y,HUANG Y Q.Application of Genetic Algorithm in the PID Parameter Optimization[J].Instrumentation Technology,2010(5):49-52.