Position Control of Mobile Robot for Human-Following in Intelligent Space with Distributed Sensors
Position Control of Mobile Robot for Human-Following in Intelligent Space with Distributed Sensors
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DOI：
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发表时间：
2006-04
影响因子：
3.2
通讯作者：
Taeseok Jin;Jangmyung Lee;H. Hashimoto
中科院分区：
文献类型：
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作者：
Taeseok Jin;Jangmyung Lee;H. Hashimoto
硬件技术的最新进展以及移动机器人和人工智能研究的最新进展可用于开发自主分布式监控系统。移动服务机器人需要感知其当前位置，以便与人类共存并在人口稠密的环境中有效地支持人类。为了实现这些能力，机器人需要跟踪环境的相关变化。为了实现这些目标，本文提出了一种利用分布式智能网络设备（DIND）在智能空间（ISpace）中使用图像来定位移动机器人的方法。该方案结合了使用航位推算传感器观测到的位置的数据和使用移动物体（例如行走的人）的图像估计的位置的数据，用于确定移动机器人的移动位置。假设运动物体是点物体，并投影到图像平面上，形成几何约束方程，根据智能空间的运动学提供物体的位置数据。利用运动物体的先验已知路径和透视相机模型，导出表示运动物体的图像帧坐标与机器人的估计位置之间关系的几何约束方程。该方法利用观测图像坐标与估计图像坐标之间的误差来定位移动机器人，并使用卡尔曼滤波方案来估计移动机器人的位置。所提出的方法应用于 ISpace 中的移动机器人，以显示确定移动机器人位置时不确定性的减少。通过计算机模拟和实验验证了其性能。
Latest advances in hardware technology and state of the art of mobile robot and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. And mobile service robot requires the perception of its present position to coexist with humans and support humans effectively in populated environments. To realize these abilities, robot needs to keep track of relevant changes in the environment. This paper proposes a localization of mobile robot using the images by distributed intelligent networked devices (DINDs) in intelligent space (IS pace) is used in order to achieve these goals. This scheme combines data from the observed position using dead-reckoning sensors and the estimated position using images of moving object, such as those of a walking human, used to determine the moving location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates of a moving object and the estimated position of the robot are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used to estimate the location of moving robot. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in the determining of the location of the mobile robot. Its performance is verified by computer simulation and experiment.