Design of Underwater Humanoid Flexible Manipulator Motion Control System Based on Data Glove

Xu, Zhen; Yang, Canjun; Wu, Weitao; Wei, Qianxiao

doi:10.1109/icmre49073.2020.9065105

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been some studies using five-finger graspers, but most of them studied hardware or other aspects [ 26 , 27 , 28 , 29 ]. The studies used for application have used a virtual space as a result of the study [ 30 , 31 ] or used algorithms for gripping pose prediction [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Chen

2023

Sensors

View full text Add to dashboard Cite

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

show abstract

Section: Introductionmentioning

confidence: 99%

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Chen

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In previous research, some research teams have developed a tendon-driven gripper and an SMA-driven flexible gripper applied to a small underwater robot (Wu et al , 2020; Xu et al , 2020). It possesses good adaptability and is able to interact effectively with the environment.…”

Section: Introductionmentioning

confidence: 99%

A flexible gripper with a wide-range variable stiffness structure based on shape memory alloy

Yang

et al. 2022

Self Cite

View full text Add to dashboard Cite

Purpose Variable stiffness structure can significantly improve the interactive capabilities of grippers. Shape memory alloys have become a popular option for materials with variable stiffness structures. However, its variable stiffness range is limited by its stiffness in two phases. The purpose of this paper is to enhance the manipulation capabilities of tendon-driven flexible grippers by designing a wide-range variable stiffness structure. Design/methodology/approach Constitutive models of shape memory alloy and mechanical models are used to analyze the performance of the variable stiffness structure. A separated solution was used to combine the tendon-driven gripper and the variable stiffness structure. The feed-forward control algorithm is used to enhance the control stability of the variable stiffness structure. Findings The stiffness variable capability of the proposed variable stiffness structure is verified by experiments. The stability of the feedback control algorithm was verified by sinusoidal tracking experiments. The variable stiffness range of 8.41 times of the flexible gripper was tested experimentally. The interaction capability of the variable stiffness flexible gripper is verified by the object grasping experiments. Originality/value A new wide-range variable stiffness structure is proposed and validated. The new variable stiffness structure has a larger range of stiffness variation and better control stability. The new flexible structure can be applied to conventional grippers to help them gain stiffness variable capability and improve their interaction ability.

show abstract

“…Flexible manipulator has been widely used in aerospace, industrial, and medical field and garnered extensive attention in recent years due to its light weight, strong flexibility, fast speed and low energy consumption compared with rigid manipulator (Chen et al, 2018; Gao et al, 2019; Hizarci and İkizoğlu, 2019; Mejerbi et al, 2018; Xu et al, 2020). In order to realize accurate control, it is indispensable to establish accurate dynamic to describe the flexible manipulator system.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered boundary quantization control for flexible manipulator based on partial differential equations dynamic model

Wang

Liu

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, we consider boundary control for a single-link flexible manipulator system described by partial differential equations (PDEs). Existing researches on controller design rarely consider the problem of communication capacity constrains during signal transmission. To deal with this problem, an adaptive control is designed to achieve the input quantization by using the random quantizer. Besides, a triggering event is addressed on the basis of relative threshold strategy for relieving communication load between controller and actuator. The proposed scheme is able to ensure that all closed-loop signals are globally uniformly bounded, and the angular tracking error and vibration converge to a residual set. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

show abstract

Design of Underwater Humanoid Flexible Manipulator Motion Control System Based on Data Glove

Cited by 6 publications

References 8 publications

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

A flexible gripper with a wide-range variable stiffness structure based on shape memory alloy

Event-triggered boundary quantization control for flexible manipulator based on partial differential equations dynamic model

Contact Info

Product

Resources

About