摘要
为了解决未知环境下多机器人协作目标跟踪问题,设计了一种基于协方差交集数据融合的分布式解决算法.单台机器人运用全协方差扩展式卡尔曼滤波器完成未知环境下机器人状态和目标状态的同步估计,当单台机器人发现同伴并利用观测值对同伴机器人状态进行本地估计后,将结果连同目标状态一起发往同伴机器人,同伴机器人进行数据验证后,采用基于协方差交集的数据融合算法完成本地相关状态的更新,由于并不需要知道相关估计对象之间的协方差阵,因此算法具有分布式特点.仿真实验证明了算法能够有效提高机器人对于自身状态、环境特征状态以及目标状态的估计准确性.
In order to solve the problem of multi-robot cooperative object tracking in unknown environments, a distributed algorithm based on covariance intersection data fusion was proposed in this paper. To the single robot, states of robot and object in an unknown environment are simultaneously estimated using a full covariance extended Kalman filter. As the robot finds a partner, it will estimate the state of the partner according to observation and then send the state collectively with object state to the partner. Once the partner verifies the incoming information, the relevant local state is updated u- sing data fusion algorithm based on a covariance intersection. Since the covariance between different states is not nee- ded, and therefore the algorithm was distributed. The improvement in estimation precision of the robot state, environ- ment characteristic state and object state using this approach was verified through the simulation results.
出处
《智能系统学报》
CSCD
北大核心
2013年第1期66-73,共8页
CAAI Transactions on Intelligent Systems
关键词
机器人
多机器人协作
目标跟踪算法
协方差交集
robots
multi-robot cooperation
object tracking algorithm
covariance intersection
