Proactive collision avoidance measures are imperative in environments where humans and robots coexist. Moreover, the introduction of high quality legged robots into workplaces highlighted the crucial role of a robust, fully autonomous safety solution for robots to be viable in shared spaces or in co-existence with humans. This article establishes for the first time ever an innovative Detect-Track-and-Avoid Architecture (DTAA) to enhance safety and overall mission performance. The proposed novel architectyre has the merit ot integrating object detection using YOLOv8, utilizing Ultralytics embedded object tracking, and state estimation of tracked objects through Kalman filters. Moreover, a novel heuristic clustering is employed to facilitate active avoidance of multiple closely positioned objects with similar velocities, creating sets of unsafe spaces for the Nonlinear Model Predictive Controller (NMPC) to navigate around. The NMPC identifies the most hazardous unsafe space, considering not only their current positions but also their predicted future locations. In the sequel, the NMPC calculates maneuvers to guide the robot along a path planned by D$^{*}_{+}$ towards its intended destination, while maintaining a safe distance to all identified obstacles. The efficacy of the novelly suggested DTAA framework is being validated by Real-life experiments featuring a Boston Dynamics Spot robot that demonstrates the robot's capability to consistently maintain a safe distance from humans in dynamic subterranean, urban indoor, and outdoor environments.

翻译：在人类与机器人共存的环境中，主动防撞措施至关重要。此外，高品质腿式机器人进入工作场所，突显了机器人要在共享空间或与人类共存环境中可行，必须具备鲁棒、完全自主的安全解决方案。本文首次提出一种创新的检测-跟踪-规避架构（DTAA），以提升安全性与整体任务性能。该新颖架构的优越性在于：整合了基于YOLOv8的目标检测、利用Ultralytics嵌入式目标跟踪，以及通过卡尔曼滤波器对跟踪目标进行状态估计。此外，采用一种新颖的启发式聚类方法，以促进对多个位置邻近且速度相似的物体进行主动规避，从而为非线性模型预测控制器（NMPC）创建一系列需规避的不安全空间。NMPC在识别最具危险性的不安全空间时，不仅考虑其当前位置，还预测其未来位置。随后，NMPC计算机动策略，引导机器人沿D$^{*}_{+}$规划的路径前往目标目的地，同时与所有已识别障碍物保持安全距离。通过采用波士顿动力Spot机器人进行的真实环境实验，验证了所提出的DTAA框架的有效性。实验证明，该机器人能够在动态地下环境、城市室内及室外环境中持续保持与人类的安全距离。