This paper addresses the problem of real-time position and orientation estimation of networked mobile robots in two-dimensional Euclidean space with simultaneous tracking of a rigid unknown object based on exterocep...This paper addresses the problem of real-time position and orientation estimation of networked mobile robots in two-dimensional Euclidean space with simultaneous tracking of a rigid unknown object based on exteroceptive sensory information extracted from distributed vision systems. The sufficient and necessary conditions for team localization are proposed. A localization and object tracking approach based on statistical operators and graph searching algorithms is presented for a team of robots localized with het- erogeneous sensors. The approach was implemented in an experimental platform consisting of car-like mobile robots equipped with omnidirectional video cameras and IEEE 802.11b wireless networking. The experimental results validate the approach.展开更多
This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate s...This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.展开更多
多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述...多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述.首先,简要介绍了基于完全连通通信条件的集中式CSLAM算法的特点和缺陷;其次,结合多机器人系统初始相对位姿关系未知的情况,从地图配准、数据关联和地图融合等三个方面,对基于通信范围或者带宽受限条件的分布式CSLAM算法的地图合并问题进行了分析和阐述;进而重点对考虑稀疏–动态通信状况的分布式CSLAM算法的最新研究成果进行了归纳总结.最后指出多机器人CSLAM研究领域今后的研究方向.展开更多
文摘This paper addresses the problem of real-time position and orientation estimation of networked mobile robots in two-dimensional Euclidean space with simultaneous tracking of a rigid unknown object based on exteroceptive sensory information extracted from distributed vision systems. The sufficient and necessary conditions for team localization are proposed. A localization and object tracking approach based on statistical operators and graph searching algorithms is presented for a team of robots localized with het- erogeneous sensors. The approach was implemented in an experimental platform consisting of car-like mobile robots equipped with omnidirectional video cameras and IEEE 802.11b wireless networking. The experimental results validate the approach.
文摘This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.
文摘多机器人系统的通信状况能够直接影响协作同时定位与地图创建(Cooperative simultaneous localization and mapping,CSLAM)算法的设计和实现.根据对多机器人通信状况所作出假设的侧重点不同,对多机器人CSLAM算法研究现状和进展进行综述.首先,简要介绍了基于完全连通通信条件的集中式CSLAM算法的特点和缺陷;其次,结合多机器人系统初始相对位姿关系未知的情况,从地图配准、数据关联和地图融合等三个方面,对基于通信范围或者带宽受限条件的分布式CSLAM算法的地图合并问题进行了分析和阐述;进而重点对考虑稀疏–动态通信状况的分布式CSLAM算法的最新研究成果进行了归纳总结.最后指出多机器人CSLAM研究领域今后的研究方向.
