Shared Control in pHRI: Integrating Local Trajectory Replanning and Cooperative Game Theory

In this article, we propose a two-stage shared control framework for physical human–robot interaction (pHRI) that addresses the inconsistency of human–robot commands and consider the influence of environmental information. In the human–robot–environment system, based on the human intention measured by the interaction force, autonomy will actively initiate the replanning when the human control intention is strong, generating a feasible local desired trajectory of the robot. At the same time, we define an index called predicted safety index (PSI) to measure the safety of the system status. When the human has control intention but does not reach the threshold, we propose a shared controller based on cooperative-game theory and PSI. Specially, it is designed within the model predictive control framework, utilizing cooperative game theory to analyze human–robot interaction behavior and treating the Pareto optimal solution as the control input. We conduct comparative experiments to evaluate the assistive performance of the proposed shared control algorithm through a waypoint tracking task with naive human users. User study with objective and subjective measures demonstrate that the algorithm effectively reduces human effort while maintaining tracking accuracy, thus enhancing both performance and safety.