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In advanced manufacturing, strict safety guarantees are required to allowhumans and robots to work together in a shared workspace. One of the challengesin this application field is the variety and unpredictability of humanbehavior, leading to potential dangers for human coworkers. This paper presentsa novel control framework by adopting safety-critical control and uncertaintyestimation for human-robot collaboration. Additionally, to select the shortestpath during collaboration, a novel quadratic penalty method is presented. Theinnovation of the proposed approach is that the proposed controller willprevent the robot from violating any safety constraints even in cases wherehumans move accidentally in a collaboration task. This is implemented by thecombination of a time-varying integral barrier Lyapunov function (TVIBLF) andan adaptive exponential control barrier function (AECBF) to achieve a flexiblemode switch between path tracking and collision avoidance with guaranteedclosed-loop system stability. The performance of our approach is demonstratedin simulation studies on a 7-DOF robot manipulator. Additionally, a comparisonbetween the tasks involving static and dynamic targets is provided.

Adaptive Safety-critical Control with Uncertainty Estimation for Human-robot Collaboration