自动化学院4月11日学术报告预告
来源： 发布日期：2013-04-10
题目：Cooperative Target Tracking of Multiple Robots
日期：2013年4月11日 上午10:00-11:30
地点：六号教学楼3层会议室
Abstract: This talk will focus on the problem of moving target tracking with a group of mobile robots. Each robot in the group has a pan/tilt camera to detect the target and has limited communication capability to communicate with neighbor robots. The problem is solved by separating it into two parts. One part is the estimation of target position and another is the flocking control of multiple robots moving towards the estimated position. We will focus on the estimation part in this talk. A novel distributed Kalman filter is developed to estimate the target position. The distributed Kalman filter is deduced based on a standard Kalman filter by modeling the neighbor's information as one of measurements. In the motion control part, a brief distributed flocking algorithm is used to track the estimated target and avoid collision. Finally the tracking algorithms are simulated with 2D and 3D robots to verify their performance. A group of three real ground mobile robots is used to test the proposed algorithm.
谷东兵教授目前在英国Essex大学计算机科学与电子工程学院工作，博士生导师。分别于1985和1988年在北京理工大学自动化系获得学士和硕士学位，于2004年在英国埃塞克斯大学计算机系获博士学位。谷东兵教授的研究方向包括机器学习、机器人感知与控制、图像处理等，在世界一流期刊如IEEE Transaction系列和国际会议上发表论文100多篇，学术成就显著。他目前是国际期刊Int. J. of model, identification, and control的编辑，是10多个IEEE 国际会议的组委会成员和50多个国际会议的程序委员会成员，是20多个IEEE国际会议的分会主席，在国际学术界有着广泛的联系和较高的知名度。
邀请单位：北京理工大学自动化学院
“复杂系统智能控制与决策”国家重点实验室（培育基地）
日期：2013年4月11日 上午10:00-11:30
地点：六号教学楼3层会议室
Abstract: This talk will focus on the problem of moving target tracking with a group of mobile robots. Each robot in the group has a pan/tilt camera to detect the target and has limited communication capability to communicate with neighbor robots. The problem is solved by separating it into two parts. One part is the estimation of target position and another is the flocking control of multiple robots moving towards the estimated position. We will focus on the estimation part in this talk. A novel distributed Kalman filter is developed to estimate the target position. The distributed Kalman filter is deduced based on a standard Kalman filter by modeling the neighbor's information as one of measurements. In the motion control part, a brief distributed flocking algorithm is used to track the estimated target and avoid collision. Finally the tracking algorithms are simulated with 2D and 3D robots to verify their performance. A group of three real ground mobile robots is used to test the proposed algorithm.
主讲人简介：
邀请单位：北京理工大学自动化学院
“复杂系统智能控制与决策”国家重点实验室（培育基地）