Unifying Obstacle Avoidance and Tracking Control of Redundant Manipulators Subject to Joint Constraints: A New Data-Driven Scheme

• Published in: IEEE transactions on cognitive and developmental systems Vol. 16; no. 5; pp. 1861 - 1871
• Main Authors: Yu, Peng, Tan, Ning, Zhong, Zhaohui, Hu, Cong, Qiu, Binbin, Li, Changsheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collision avoidance; Constraints; Data-driven control; End effectors; Jacobian matrices; Kinematics; Manipulator Jacobians; Obstacle avoidance; obstacle avoidance (OA); Quadratic programming; Recurrent neural networks; Redundancy; redundant manipulator; Robot arms; Robots; Task analysis; Tracking control; Trajectory; zeroing neural network (ZNN)
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2024.3387575

In modern manufacturing, redundant manipulators have been widely deployed. Performing a task often requires the manipulator to follow specific trajectories while avoiding surrounding obstacles. Different from most existing obstacle-avoidance (OA) schemes that rely on the kinematic model of redundant manipulators, in this article, we propose a new data-driven obstacle-avoidance (DDOA) scheme for the collision-free tracking control of redundant manipulators. The OA task is formulated as a quadratic programming problem with inequality constraints. Then, the objectives of obstacle avoidance and tracking control are unitedly transformed into a computation problem of solving a system including three recurrent neural networks. With the Jacobian estimators designed based on zeroing neural networks, the manipulator Jacobian and critical-point Jacobian can be estimated in a data-driven way without knowing the kinematic model. Finally, the effectiveness of the proposed scheme is validated through extensive simulations and experiments.