Abstract: For robot in unknown environment, using particle swarm optimization algorithm to simultaneous localization and mapping (SLAM), error location accuracy is not high, direction error as well as the problems of poor robustness, puts forward an improved SLAM method based on chaos theory in chicken swarm algorithm. First, introduced the study mechanism of chicken swarm algorithm, through learning coefficient of chicken swarm algorithm in chaotic mutation, then using chaotic search to disturbance the chicken swarm of each subgroup, at the same time in the optimal location of the adaptive chaotic search to find the population within the territory of the optimal location. The algorithm is simulated and compared with the SLAM algorithm based on particle swarm optimization. The simulation results show that the proposed algorithm can obtain higher positioning accuracy and precision of map building and better estimation stability.

Key words: SLAM, chicken swarm optimization, quadruped robot, robustness, chaos

摘要： 针对机器人在未知环境下采用粒子群优化算法进行同时定位与地图构建(Simultaneous Localization And Mapping, SLAM)时，定位精度不高、方向误差较大以及鲁棒性差的问题，提出一种利用混沌理论优化改进鸡群(Chicken Swarm Optimization, CSO)的SLAM算法。首先在鸡群算法中引入小鸡的学习机制，对小鸡的学习系数进行混沌变异，其次引入混沌序列对鸡群中各子群进行扰动，同时在个体最优位置的领域内进行自适应混沌搜索，以寻找子群的最优位置。对所提出的机器人SLAM算法进行了实验验证，并与基于粒子群优化的SLAM算法进行比较，得出该算法不仅能够获得较高的定位精度和地图构建精度还具有较好的估计稳定性。

关键词: SLAM, 鸡群优化算法, 四足机器人, 鲁棒性, 混沌