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

Peng Yu, Ning Tan*, Zhaohui Zhong, Cong Hu, Binbin Qiu, Changsheng Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1861-1871
Number of pages11
JournalIEEE Transactions on Cognitive and Developmental Systems
Volume16
Issue number5
DOIs
Publication statusPublished - 2024

Keywords

  • Data-driven control
  • obstacle avoidance (OA)
  • redundant manipulator
  • zeroing neural network (ZNN)

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