Collision detection via visual fences can significantly enhance the safety of collaborative robotic arms. Existing work typically performs such detection based on pre-deployed stationary cameras outside the robotic arm's workspace. These stationary cameras can only provide a restricted detection range and constrain the mobility of the robotic system. To cope with this issue, we propose an active sense method enabling a wide range of collision risk evaluation in dynamic scenarios. First, an active vision mechanism is implemented by equipping cameras with additional degrees of rotation. Considering the uncertainty in the active sense, we design a state confidence envelope to uniformly characterize both known and potential dynamic obstacles. Subsequently, using the observation-based uncertainty evolution, collision risk is evaluated by the prediction of obstacle envelopes. On this basis, a Markov decision process was employed to search for an optimal observation sequence of the active sense system, which enlarges the field of observation and reduces uncertainties in the state estimation of surrounding obstacles. Simulation and real-world experiments consistently demonstrate a 168% increase in the observation time coverage of typical dynamic humanoid obstacles compared to the method using stationary cameras, which underscores our system's effectiveness in collision risk tracking and enhancing the safety of robotic arms.

翻译：通过视觉围栏进行碰撞检测能显著提升协作机械臂的安全性。现有工作通常依赖部署在机械臂工作区外的固定式预装摄像头进行此类检测，但固定摄像头仅能提供受限的检测范围，且制约了机器人系统的移动性。为解决该问题，我们提出一种主动感知方法，可在动态场景中实现大范围碰撞风险评估。首先，通过为摄像头增加旋转自由度实现主动视觉机制。针对主动感知中存在的观测不确定性，设计状态置信包络以统一表征已知与潜在动态障碍物。进而，基于观测驱动的状态不确定性演化规律，通过预测障碍物包络来评估碰撞风险。在此基础上，采用马尔可夫决策过程搜索主动感知系统的最优观测序列，从而扩大观测视场并降低周围障碍物状态估计的不确定性。仿真与真实实验表明，与固定摄像头方法相比，本系统的典型动态仿人障碍物观测覆盖时间提升168%，充分验证了该系统在碰撞风险跟踪与增强机械臂安全性方面的有效性。