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

Abstract

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. INDEX TERMS Velocity observer, multiple redundant manipulators, recurrent neural network, motion planning, zeroing neural network.

Bi-criteria Acceleration Level Obstacle Avoidance of Redundant Manipulator

“…Series of related products have been reported, e.g., in Li et al ( 2016 ), from the perspective of game theory, and a distributed recurrent neural network (RNN)-based dynamic controller was proposed for the coordination control of multi-robot system. In Li et al ( 2020 ), based on the RNN, Li et al investigated the kinematic control problem of the multi-robot system under neighbor-to-neighbor communication. To access the desired global command, an observer was developed for estimating the velocity information of the desired motion trajectory.…”

Recurrent Neural Network for State Adjustment of Redundant Manipulators

“…State adjustment refers to the process of moving the manipulator from the current state to the target state [26] without the displacement of the end-effector. Most of the research on the redundant manipulator focuses on motion planning such as the manipulability optimization method stated in [27] during the task execution of the redundant manipulator, but there are few achievements on the problem of state adjustment [28]- [30]. In fact, state adjustment is imperative in certain situations [31], [32].…”

Analysis and Verification on the Equivalence Between Jerk-Level and Acceleration-Level Schemes Applied to Manipulators Controlling

“…For a long time, robots have been a concern for many researchers, and their development has led to significant revolutions in many fields [1]- [3], such as the automated production that has already emerged in the industrial field and has broken the limits of pure manual production [4]- [7]. Besides, a particular class of robots called redundant manipulators possesses more degrees-of-freedom than needed to satisfy some extra performance indicators while finishing the primary task [8]- [10]. Redundant manipulators are mainly applied to accomplish tasks that are difficult for human beings or time-consuming, thus reducing unpredictable problems caused by operators in precision and durability [11], [12].…”