Adaptive Safety-Critical Control With Uncertainty Estimation for Human–Robot Collaboration

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.
Note to Practitioners
—This research addresses the need to improve the safety of robots interacting with humans when performing collaborative tasks. Existing safety-critical control (SCC) approaches do not adequately monitor and continuously limit the state of the robot in Cartesian space, which results in a risk of injury to human operators if there is unexpected behavior during collaboration. Additionally, existing SCC approaches only consider system uncertainty for a single task (i.e. path tracking only or collision avoidance only). These problems limit the applicability of SCC techniques to manufacturing cobots. We address these problems by developing a controller that accounts for uncertainty in robot dynamics, guarantees that the robot end-effector remains within a constrained task space, and continuously modifies its motion in real-time to avoid dynamic obstacles that violate this space. We employ a machine learning approach to estimate the unknown uncertainties in real-time, allowing them to be incorporated within the controller design. The designed controller selects the shortest path for collision avoidance at each sample instant in order to minimize the total motion of the robot.