Invariance Control for Safe Human-Robot Interaction in Dynamic Environments

In human-robot interaction, it is essential to ensure that the robot poses no threat to the human. Especially in applications that require close or physical interaction, e.g., collaborative manufacturing or rehabilitation, the danger emanating from the robot has to be minimized. Control schemes introducing virtual constraints have proven valuable in this context since they allow us to define a safe zone to move in without endangering the human. Combining the different requirements on the control scheme, such as real-time capability, stability, and reliability, in the presence of external disturbances and dynamic limits, however, turns out to be challenging. In this paper, we present a novel control scheme for human-robot interaction, which enforces dynamic constraints even in the presence of external forces. Based on an analytic constraint description and a feedback linearization of the system dynamics, a safe set of states is determined, which is then rendered controlled positively invariant, thus keeping the system in a safe configuration. The controlled system is analyzed with respect to invariance and boundedness with the results being illustrated in a full-scale experiment.