【摘要】 为有效识别和剔除多机器人协同定位时的冲突相对观测信息,使协同定位中各机器人所利用的信息更具一致性、提高协同定位的实时性和有效性,提出融合完全信息静态博弈的EKF协同定位算法。该方法运用静态博弈理论对机器人间的相对观测信息冲突进行识别,依据博弈所获得的一致相对观测信息进行协同定位。针对机器人之间相对观测信息存在单向观测和双向观测2种类型,分别推导出不同的协同定位公式。仿真实验结果表明:算法使机器人团队在协同定位时能更有效率地实现信息共享,在保证协同定位精度提高的同时减少计算量。更多还原

【Abstract】 For recognizing and eliminating the conflicting observations of multi-robot cooperative localization, while improving its consistency and effectiveness, a new cooperative localization algorithm which merged static game with complete information into EKF(extended Kalman filter) was proposed. The proposed algorithm uses the game theory to check the relative observations. After eliminating the conflicting relative observations, cooperative localization can be more effective. Since relative observations can be classified into two types, i.e one-sided relative observations and bidirectional relative observations, two sets of EKF cooperative localization formulations were respectively deduced. The simulation results show that the proposed algorithm makes the robot team only share coherent relative observations between them. It ensures the improvement of localization accuracy of every robot and reduces the computational complexity at the same time.更多还原