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Research Papers

Control of Adaptive Switching in the Sensing-Executing Mode Used to Mitigate Collision in Robot Force Control

[+] Author and Article Information
Hongli Cao

College of Automotive Engineering,
Chongqing University,
Shazheng Street No. 174,
Chongqing 400044, China
e-mail: 20160701027@cqu.edu.cn

Ye He

Professor
State Key Laboratory of Mechanical
Transmission,
Chongqing University,
Shazheng Street No. 174,
Chongqing 400044, China
e-mail: hifish2@gmail.com

Xiaoan Chen

Professor
State Key Laboratory of Mechanical
Transmission,
Chongqing University,
Shazheng Street No. 174,
Chongqing 400044, China
e-mail: xachen@cqu.edu.cn

Zhi Liu

College of Automotive Engineering,
Chongqing University,
Shazheng Street No. 174,
Chongqing 400044, China
e-mail: liuzhierd@hotmail.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received March 4, 2019; final manuscript received May 17, 2019; published online June 27, 2019. Assoc. Editor: Richard Bearee.

J. Dyn. Sys., Meas., Control 141(11), 111003 (Jun 27, 2019) (12 pages) Paper No: DS-19-1103; doi: 10.1115/1.4043917 History: Received March 04, 2019; Revised May 17, 2019

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

Copyright © 2019 by ASME
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Figures

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Fig. 1

Contact model of the robot and the environment: (a) description of the contact model and (b) contact force varying in the robot from free space to steady contact space

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Fig. 2

Implementation of impedance control

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Fig. 3

Implementation of admittance control

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Fig. 4

Implementation of hybrid control

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Fig. 5

Performance comparison of impedance, admittance, and hybrid control: (a) the contact force during force tracking and (b) position tracking corresponding to force tracking

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Fig. 7

Adaptive switching control strategy to mitigate collision forces

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Fig. 8

The ant bionic control system for easing robot collision in force tracking for uncertain environments

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Fig. 9

Compare the original and new detecting devices: (a) the original device actuator is a linear motor, which receives the sine signal to produce motion and force and (b) the new device actuator is a PZT stack + displacement amplifier

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Fig. 10

The principle diagram of the self-sensing strain bridge

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Fig. 11

Different switching time for mitigating the collision force

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Fig. 12

The effect of mitigating collision force on the desired stiffness control parameters Kd

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Fig. 13

The adaptive control parameter Kd corresponding to environmental stiffness

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Fig. 14

Performance comparison of the adaptive and constant impedance control

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Fig. 15

Performance comparison of the adaptive and constant admittance control

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Fig. 16

Performance comparison of the adaptive and constant hybrid control

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Fig. 18

Performance comparison of self-sensing and adaptive signal separation

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Fig. 19

Gain variation of self-sensing and adaptive signal separation

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