Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This reduces walking speed, and the constant need for vigilance increases fatigue and, thus, the risk of accidents. This work presents a virtual leash with which a robot can naturally follow an operator. We use a sensor fusion based on a custom-built RF transponder, RGB cameras, and a LiDAR. In addition, we customize a local avoidance planner for legged platforms, which enables us to navigate dynamic and narrow environments. We successfully validate on the ANYmal platform the robustness and performance of our entire pipeline in real-world experiments.

翻译：个人移动机器人助手预计将在工业和医疗保健领域得到广泛应用。例如，行动不便者可通过机器人辅助完成日常任务，建筑工人可借助机器人执行现场精密监测工作。然而，在移动过程中手动操控机器人需要操作者高度集中注意力，尤其在狭窄或拥挤空间内更为明显。这不仅会降低行走速度，持续保持警惕还会加剧操作疲劳，从而增加事故风险。本研究提出一种虚拟牵引系统，使机器人能够自然地跟随操作者。我们采用基于定制射频应答器、RGB相机与激光雷达的传感器融合方案，并针对足式平台定制了局部避障规划器，从而实现在动态狭窄环境中的自主导航。通过在ANYmal平台上开展真实场景实验，我们成功验证了整套系统的鲁棒性与性能表现。