Mingdi Deng

ORCID: 0000-0003-2629-0096
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About
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Research Areas
  • Prosthetics and Rehabilitation Robotics
  • Muscle activation and electromyography studies
  • Robot Manipulation and Learning
  • Stroke Rehabilitation and Recovery
  • Robotic Path Planning Algorithms
  • Robotic Locomotion and Control
  • Teleoperation and Haptic Systems

South China University of Technology
2017-2018

In this paper, a physical human-robot interaction approach is presented for the developed robotic exoskeleton using admittance control to deal with human subject's intention as well unknown inertia masses and moments in dynamics. The represented by reference trajectory when complying external force. Online estimation of stiffness employed variable impedance property exoskeleton. Admittance first based on measured force order generate tasks. Then, adaptive proposed uncertain dynamics...

10.1109/tie.2018.2821649 article EN IEEE Transactions on Industrial Electronics 2018-03-30

Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot achieve cooperative manipulation humans. The consists two layers. lowlevel the upper limb robot, admittance asymmetric barrier Lyapunov function-based adaptive neural network controller proposed enable be back drivable. order high-level interaction, strategy for learning human skills from demonstration by...

10.1109/tcyb.2018.2864784 article EN IEEE Transactions on Cybernetics 2018-08-31

In order to extend promising robot applications in human daily lives, robots need perform dextrous manipulation tasks, particularly for a mobile dual-arm robot. This paper propose novel control strategy, which consists of first trial process and learning phase, enable complete grasp-and-place task can be decomposed into movement sequences, such as reaching, grasping, cooperative grasped object. Under the guidance vision system, with physical constraints successfully fulfills by tracking...

10.1109/tcds.2018.2868921 article EN IEEE Transactions on Cognitive and Developmental Systems 2018-09-06

This paper presents a state-of-art reinforcement learning strategy to enable human-like dual arm mobile robot deal with some complicated tasks cooperation. A complex movement task of can be divided into action phases and subgoals, each which corresponds dynamic motion primitive (DMP). During control the humanoid-like robot, there are noises that affect precision robot. To those uncertain perturbations while handling varying manipulation dynamics for grasping motion, (RL) algorithm sequences...

10.1109/icarm.2017.8273159 article EN 2017-08-01
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