Abstract：Aiming at the three main problems in localization of mobile robots, position tracking, global localization and kidnapped problem, an autonomous localization strategy based on genetic algorithm is proposed. A fitness function is designed based on the similarity of position. The real-coded method is used in the crossover and the mutation steps to improve the real-time ability of the algorithm. For the kidnapped problem, a scattering mechanism is introduced into the regular genetic algorithm. Thus, the population impoverishment problem is largely alleviated. Subsequently, the population state is updated with the kinematic model to achieve continuous localization of mobile robots. The experimental results of indoor environment demonstrate the validity of the proposed localization strategy.
[1] Thrun S, Fox D, Burgard W, et al. Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence, 2001, 128(1): 99-141,143 [2] Kwon S J, Yang K W, Park S. An Effective Kalman Filter Localization Method for Mobile Robots // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China, 2006: 1524-1529 [3] Fox D, Burgard W, Thrun S. Markov Localization for Mobile Robots in Dynamic Environments. Journal of Artificial Intelligence Research, 1999, 11: 391-427 [4] Fox D, Burgard W, Dellaert F, et al. Monte Carlo Localization: Efficient Position Estimation for Mobile Robots // Proc of the National Conference on Artificial Intelligence. Orlando, USA, 1999: 343-349 [5] Leonard J J, Durrant-Whyte H F. Mobile Robot Localization by Tracking Geometric Beacons. IEEE Trans on Robotics and Automation, 1991, 7(3): 376-382 [6] Wijk O, Christensen H I. Triangulation-Based Fusion of Sonar Data with Application in Robot Pose Tracking. IEEE Trans on Robotics and Automation, 2000, 16(6): 740-752 [7] Zhou Min, Sun Shudong. Genetic Algorithm: Theory and Application. Beijing, China: National Defence Industry Press, 1999 (in Chinese) (周 明,孙树栋.遗传算法原理及应用.北京:国防工业出版社, 1999) [8] Xuan Guangnan, Cheng Runwei, Yu Xinjie, et al. Genetic Algorithms and Engineering Optimization. Beijing, China: Tsinghua University Press, 2004 (in Chinese) (玄光男,程润伟,于歆杰,等.遗传算法与工程优化.北京:清华大学出版社, 2004) [9] Rfer T, Jüngle M. Vision-Based Fast and Reactive Monte Carlo Localization // Proc of the IEEE Conference on Robotics and Automation. Taipei, China, 2003, Ⅰ: 856-861 [10] Rekleitis I, Dudek G, Milios E. Probabilistic Cooperative Localization and Mapping in Practice // Proc of the IEEE Conference on Robotics and Automation. Taipei, China, 2003, Ⅱ: 1907-1912
2009, Vol. 22 
