  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.
