Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator

Erdi Sayar; Xiang Gao; Yingbai Hu; Guang Chen; Alois Knoll

doi:10.1016/j.isatra.2024.01.007

Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator

ISA Trans. 2024 Mar:146:16-28. doi: 10.1016/j.isatra.2024.01.007. Epub 2024 Jan 9.

Authors

Erdi Sayar¹, Xiang Gao², Yingbai Hu³, Guang Chen⁴, Alois Knoll⁵

Affiliations

¹ School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany. Electronic address: erdi.sayar@tum.de.
² School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany. Electronic address: xiang.gao@tum.de.
³ School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany; Multi-Scale Medical Robotics Centre, The Chinese University of Hong Kong, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: yingbai.hu@tum.de.
⁴ School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany; School of Automotive Engineering and the Department of Computer Science, Tongji University, Shanghai, China. Electronic address: guangchen@tongji.edu.cn.
⁵ School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany. Electronic address: knoll@in.tum.de.

PMID: 38228436
DOI: 10.1016/j.isatra.2024.01.007

Abstract

This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the BI²RRT^∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) ²which is open access and open source integrative simulation framework powered by Gazebo and developed by our team.

Keywords: Mobile manipulator; Neural networks; Optimization; Robot control.