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Vision-assisted adaptive target tracking of unmanned ground vehicles

机译：视觉辅助的无人地面车辆自适应目标跟踪

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In this paper, we develop a vision-based adaptive control algorithm for target tracking from a collaborative UAV-UGV platform. By estimating global camera motion and registering video frames into a common coordinate system, the UAV detects moving targets, as well as the UGV on the ground. It estimates their locations and sends this information to the UGV to guide its tracking of the targets. Based on this vision-based location estimation, we develop an adaptive fault-tolerant control scheme for the UGV to accurately track the target. We have designed an adaptive controller to adjust the control currents to the motors which are mounted on its left and right wheels so that the vehicle is able to follow the target trajectory. We investigate actuator fading of the vehicle, unknown parameters in the vehicle system model, together with the impact of the uncertainty and noise in computer vision processing on the overall tracking performance. Our simulation results demonstrate that the proposed algorithm is very efficient.
机译：在本文中，我们开发了一种基于视觉的自适应控制算法，用于从协作式UAV-UGV平台进行目标跟踪。通过估计全球摄像机的运动并将视频帧注册到一个公共坐标系中，UAV可以检测到移动目标以及地面上的UGV。它估计它们的位置，并将此信息发送给UGV，以指导其对目标的跟踪。基于这种基于视觉的位置估计，我们为UGV开发了一种自适应容错控制方案，以精确地跟踪目标。我们设计了一种自适应控制器，用于调节安装在左右车轮上的电机的控制电流，从而使车辆能够遵循目标轨迹。我们研究了车辆的执行器衰减，车辆系统模型中的未知参数，以及计算机视觉处理中的不确定性和噪声对整体跟踪性能的影响。仿真结果表明，该算法是非常有效的。

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