Design, analysis and control of a novel deployable grasping manipulator. (August 2019)

• Record Type: Journal Article
• Authors: Li, Guotao; Huang, Hailin; Guo, Hongwei; Li, Bing
• Is Part Of: Mechanism and machine theory. Volume 138(2019)
• Page Start: 182
• Page End: 204
• Publication Date: 2019-08
• Subjects: Deployable grasping manipulator -- Mechanism design -- Adaptive extended state observer -- Sliding mode control -- Adaptive robust control; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2019.03.043
• Languages: English
• ISSNs: 0094-114X

Abstract

Highlights: A novel deployable grasping manipulator with both deployment motion and grasping mobility is developed. The deploy/fold ratio, grasping range and grasping patterns of the manipulator are analyzed to show that it is suitable for grasping large-scale objects. A new adaptive extended state observer is proposed to estimate some terms in the dynamics of the manipulator to realize real-time dynamic feedback. A modified adaptive robust controller is proposed using the integral sliding mode surface and add-on adaptive sliding control component to improve control performance. Abstract: The robotic grasping of large-scale objects is a challenging task because it requires a large grasping mechanism and deployment motion capability. In this paper, a mechanism design scheme and a control scheme for grasping large-scale objects are proposed. First, a novel deployable grasping manipulator (DGM) is developed in which the deployment motion and grasping motion are decoupled by taking advantage of the deployable mechanism and serial-parallel mechanism. The kinematics, the performance indices and grasping patterns are analyzed to show that it is suitable for grasping large-scale objects. Second, the dynamics of the DGM is modelled, considering its computational complexity, a new adaptive extended state observer (AESO) is proposed to estimate the dynamics to realize real-time dynamic feedback, thus the computational complexity can be avoided. Third, an add-on adaptive sliding mode control component is proposed to deal with the AESO estimation errors and to improve its robustness against disturbances. Then, an AESO-based adaptive robust control controller is proposed based on the add-on adaptive sliding mode control component and the integral sliding mode surface. Finally, experiments are conducted to validate the effectiveness of the proposed controller for the DGM system in comparison with other state-of-the-art controllers. This also validates that the DGM can complete deployment motion and grasping motion.