When humans and robots operate in and occupy the same local space, proximity detection and proactive collision avoidance is of high importance. As legged robots, such as the Boston Dynamics (BD) Spot, start to appear in real-world application environments, ensuring safe robot-human interactions while operating in full autonomy mode becomes a critical gate-keeping technology for trust in robotic workers. Towards that problem, this article proposes a track-and-avoid architecture for legged robots that combines a visual object detection and estimation pipeline with a Nonlinear Model Predictive Controller (NMPC) based on the Optimization Engine, capable of generating trajectories that satisfy the avoidance and tracking problems in real-time operations where the computation time never exceeded 40 ms. The system is experimentally evaluated using the BD Spot, in a custom sensor and computational suite, and in fully autonomous operational conditions, for the robot-human safety scenario of quickly moving noncooperative obstacles. The results demonstrate the efficacy of the scheme in multiple scenarios where the maximum safety distance violation was only 9 cm for an obstacle moving at 2.5 m/s while affected by both state estimation and object detection uncertainty and noise.

中文翻译：

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用于蛇毒分析的 OHP 片上集成有碳纤维微电极的便携式电化学平台


当人类和机器人在同一局部空间中操作并占据相同的局部空间时，接近检测和主动避免碰撞非常重要。随着波士顿动力 (BD) Spot 等腿式机器人开始出现在现实世界的应用环境中，确保在完全自主模式下操作时安全的机器人与人类交互成为对机器人工人信任的关键把关技术。针对这个问题，本文提出了一种腿式机器人的跟踪和回避架构，该架构将视觉对象检测和估计管道与基于优化引擎的非线性模型预测控制器（NMPC）相结合，能够生成满足回避和回避的轨迹。跟踪实时操作中的问题，其中计算时间从未超过 40 毫秒。该系统使用 BD Spot 在定制传感器和计算套件中，在完全自主的操作条件下进行了实验评估，用于快速移动非合作障碍物的机器人与人类安全场景。结果证明了该方案在多种场景下的有效性，其中对于以 2.5 m/s 速度移动的障碍物，最大安全距离违规仅为 9 cm，同时受到状态估计和物体检测不确定性和噪声的影响。