A Game Theory Perspective for Multimanipulators With Unknown Kinematics

Multimanipulators exhibit excellence across various domains through collaboration, yet optimizing motion performance to fulfill task-specific requirements while tackling unknown kinematic models remains formidable challenges. This paper pioneers the integration of game theory into collaborative control for multi-manipulators with unknown kinematics, establishing a robust optimal framework through efficient distributed networks and strategic competition to achieve Nash equilibrium. An optimal collaboration scheme integrates hybrid multi-objective optimization and orthogonal projection repetitive motion generation (OPRMG) scheme to mitigate excessive and abrupt changes in joint velocity, enhance manipulability, and eliminate joint drift while decoupling errors between joint and Cartesian spaces. Furthermore, an adaptive power-exponentialtype neural network (APETNN) robust controller is proposed to solve the scheme, integrating learning and control components to enable manipulators to respond promptly with an optimal strategy even with incomplete or implicit kinematics. The efficacy, practicality, and superiority of the proposed framework for collaborative control of multimanipulators are demonstrated through theoretical analyses, simulations and experiments.