论文标题
超宽带教和重复
Ultra-Wideband Teach and Repeat
论文作者
论文摘要
对于许多应用，自主回答手动教学的路径是可取的，并且教授和重复（T＆R）算法提出了一种适用于远程自治的方法。在本文中，建议使用资源有限的车辆提出了基于超宽带（UWB）的T＆R。通过将单个UWB收发器固定在室内环境中的遥远未知位置，具有3个UWB收发器的机器人在教学通行证期间构建了本地一致的地图，通过将定制范围协议下的范围测量与板上IMU和高度测量值融合在一起。然后，机器人使用Teach Pass中的信息来自动地追溯相同的轨迹。所提出的范围协议和T＆R算法在模拟中验证了，其中证明机器人可以成功地通过子米跟踪误差来成功地检索教导的轨迹。
Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.
