机器人自适应主动同时定位与建图新方法

针对多机器人的定位与建图受到即时定位与地图构建(SLAM)研究方法和技术不成熟的制约问题,提出一种基于扩展卡尔曼滤波(EKF)的自适应同时定位与建图方法。首先,基于EKF估计方法,将SLAM中机器人运动方式的选取问题转化为一个多目标最优控制问题,机器人选取最优化目标函数的控制输入,从而以主动的、智能的和自适应的方式探索环境;然后,将上述方法推广到多机器人SLAM中,以实现更为准确、高效和鲁棒的定位与建图。仿真结果表明,该方法大大提高了机器人建图的效率、准确性和鲁棒性。该方法用于机器人主动同时定位和建图是可行的、有效的。

A New Approach to Active Simultaneous Localization and Mapping for Mobile Robot

As the localization and mapping of multi-robot are restricted by the immature SLAM research method and technology, a kind of adaptive method for simultaneous localization and mapping based on EKF is proposed. First of all, the selection of robot movements in SLAM is converted into a multi-objective optimal control problem based on extended kalman filter estimation method. Thus, the robot can select the control input of optimization objective function, so that the active, intelligent and adaptive way can be adopted to explore the environment. Then, the above method is generalized into the SLAM of multiple robots, in order to achieve more accurate, efficient and robust localization and mapping. The simulation results show that the method is effective.