  In advanced manufacturing, strict safety guarantees are required to allow
humans and robots to work together in a shared workspace. One of the challenges
in this application field is the variety and unpredictability of human
behavior, leading to potential dangers for human coworkers. This paper presents
a novel control framework by adopting safety-critical control and uncertainty
estimation for human-robot collaboration. Additionally, to select the shortest
path during collaboration, a novel quadratic penalty method is presented. The
innovation of the proposed approach is that the proposed controller will
prevent the robot from violating any safety constraints even in cases where
humans move accidentally in a collaboration task. This is implemented by the
combination of a time-varying integral barrier Lyapunov function (TVIBLF) and
an adaptive exponential control barrier function (AECBF) to achieve a flexible
mode switch between path tracking and collision avoidance with guaranteed
closed-loop system stability. The performance of our approach is demonstrated
in simulation studies on a 7-DOF robot manipulator. Additionally, a comparison
between the tasks involving static and dynamic targets is provided.
