Cooperative Kinematic Control for Multiple Redundant Manipulators Under Partially Known Information Using Recurrent Neural Network

In this study, we investigate the problem of cooperative kinematic control for multiple redundant manipulators under partially known information using recurrent neural network (RNN). The communication among manipulators is modeled as a graph topology network with the information exchange that only occurs at the neighbouring robot nodes. Under partially known information, four objectives are simultaneously achieved, i.e, global cooperation and synchronization among manipulators, joint physical limits compliance, neighbor-to-neighbor communication among robots, and optimality of cost function. We develop a velocity observer for each individual manipulator to help them to obtain the desired motion velocity information. Moreover, a negative feedback term is introduced with a higher tracking precision. Minimizing the joint velocity norm as cost function, the considered cooperative kinematic control is built as a quadratic programming (QP) optimization problem integrating with both joint angle and joint speed limitations, and is solved online by constructing a dynamic RNN. Moreover, global convergence of the developed velocity observer, RNN controller and cooperative tracking error are theoretically derived. Finally, under a fixed and variable communication topology, respectively, application in using a group of iiwa R800 redundant manipulators to transport a payload and comparison with the existing method are conducted. Among the simulative results, the robot group synchronously achieves the desired circle and rhodonea trajectory tracking, with higher tracking precision reaching to zero. When joint angles and joint velocities tend to exceed the setting constraints, respectively, they are constrained into the upper and lower bounds owing to the designed RNN controller.