Research on safe robot operation using force tracking impedance control
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Luna2.pptxSafe robot operation using force tracking impedance control
Luna
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contents
Introduction
Background
Impedance Control
Problem Statement
Challenges
Research Question
Aim
Objectives
Significance of the study
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Expected Outcomes
Literature Review
Methodology
Expected Results and Discussion
Conclusion
Introduction
The scope of industrial robot applications was established from the conventional handling, assembly and welding tasks leading to a wide range of production.
Such applications involves robot end-effector interaction with environment.
In interaction case, insufficient compliance for manipulator is always a key problem.
Only position control is not sufficient to control and handle the interaction.
It is necessary to develop an interaction control method that achieves position tracking and reliably adapts the force exerted (force tracking) on the environment in order to avoid damage to both environment and the manipulator.
Impedance control is a feasible solution to overcome position uncertainties while robot-environment interaction and avoid large impact forces
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Impedance control
Impedance controller resembles a virtual mass-spring-damper system between the environment and robot end-effector
This allows the robot to safely interacts with the environment.
Mechanical impedance is the ratio of force input to position output.
Impedance shows that how much the body resist the applied forces.
Mass-spring-damper system while interaction
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Challenges in impedance control
Impedance model is second order equation with desired impedance parameters.
Require optimal tuned parameters to achieve the desired interactive impedance.
To avoid parameters tuning, additional control algorithm is implemented.
This algorithm helps in minimizing the force tracking error.
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Problem Statement
Research Question
How the impedance control can be improved to enhance robot operation safety while environment interaction.
Research Aim
The aim of this research is to
Improve the force tracking impedance control structure for enhanced end-effector force tracking.
Furthermore, optimally tune the impedance parameter to make the system more sensitive for small force change.
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Problem Statement
Research Objective
To perform the proposed research, the research objectives are
Review the literature to understand the impedance model
Then update the impedance model in a way that will improve the end-effector force tracking.
Implement machine learning or optimization algorithm to optimally tune the impedance parameters.
Implement and compare the proposed and existing algorithms.
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Significance of study
For robust robot operation while interaction, integrated position and force control is utilized.
Usually, the position control is focused for improved performance. Whereas for force control, traditional impedance model is used.
Therefore, sometimes impedance results are not precise in the presence of external disturbance.
This research will then contribute specially to impedance control.
This will help in enhance the robot safety specially when human and robot are working together.
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Expected outcomes
Improvement and Implementation of Impedance Control
Will enhance the robot end-effector trajectory tracking while interaction.
This will improve the safety of robot operation
Resulting in avoiding damage to both the robot and the environment.
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Optimal impedance parameters
Will make system more sensitive to small end-effector forces.
This will help the system to easily work in sensitive environments.
Impedance Control literature
| Year | Author | Contribution |
| 1985 | Hogan | Hybrid position/force control |
| 1985 | Hogan | Impedance control |
| 1991 | Chan et al. | SMC-based impedance control |
| 1997 | Seraji et al. | Force tracking in impedance control |
| 2003 | Iwasaki et al. | Adaptive force control using sliding mode control |
| 2004 | Jung et al. | Adaptive impedance force tracking under unknown environment |
| 2008 | Lee et al. | Force tracking with variable target stiffness |
| 2018 | Liang et al. | Force tracking with unknown environment via iterative learning algorithm |
| 2019 | Yang et al. | PID-Based force tracking with nonlinear velocity observer |
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References
N. Hogan, "Impedance control: An approach to manipulation: Part I—Theory," 1985.
N. Hogan, "Impedance control: An approach to manipulation: Part II—Implementation," 1985.
F. J. Abu-Dakka, and M. Saveriano, "Variable impedance control and learning – a review" frontiers in Robotics and AI, vol. 7, 590681, 2020
S. Chan, B. Yao, W. Gao et al., "Robust impedance control of robot manipulators," Robots Automation, vol. 6, no. 4, pp. 220–227, 1991.
J. Peng, Z. Yang, and T. Ma, "Position/force tracking impedance control for robot systems with uncertainties based on adaptive jacobian and neural network," complexity, vol. 2019, p. 1406534.
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methodology
The methodology involves the following steps
Impedance Model Improvement: Analysis of different control algorithm and their comparison will help in the selection of best algorithm to improve the impedance model.
Optimization of Impedance Parameters: Utilize the machine learning or optimization algorithm such as Particle Swarm Optimization (PSO) for optimal impedance parameters.
Implementation and Comparison: Implement the final algorithms and compare with existing algorithms.
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Expected Results and discussion
After the research, the results and discussions are expected as
The improved impedance control enhanced the robot end-effector force tracking by converging the force tracking error to zero.
This is because the control algorithm integrated in impedance control provides more energy to the robot.
Furthermore, the optimization technique helped the robot detect the small change in the end-effector force based on changing environment.
This is because the algorithm has optimally tuned the parameters based on the environment dynamics.
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conclusion
Insufficient compliance while robot-environment contact is always a key problem.
This research presented a basic and easy review about the impedance control.
Mainly, conventional impedance control with adaptive algorithm and impedance control with modified position control loop is utilized.
Improved force control loop was very useful in making the robot operation safer as required.
Optimized desired impedance parameters avoided sudden force impact by detecting small force change.
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References
N. Hogan, "Impedance control: An approach to manipulation: Part I—Theory," 1985.
N. Hogan, "Impedance control: An approach to manipulation: Part II—Implementation," 1985.
S. Chan, B. Yao, W. Gao et al., "Robust impedance control of robot manipulators," Robots Automation, vol. 6, no. 4, pp. 220–227, 1991.
H. Seraji and R. Colbaugh, "Force tracking in impedance control," The International Journal of Robots Research, vol. 16, no. 1, pp. 97-117, 1997.
M. Iwasaki, N. Tsujiuchi, and T. Koizumi, "Adaptive force control for unknown environment using sliding mode control with variable hyperplane," JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol. 46, no. 3, pp. 967-972, 2003.
S. Jung, T. C. Hsia, and R. G. Bonitz, "Force tracking impedance control of robot manipulators under unknown environment," IEEE Transactions on Control Systems Technology, vol. 12, no. 3, pp. 474-483, 2004.
X. Liang, H. Zhao, X. Li, and H. Ding, "Force tracking impedance control with unknown environment via an iterative learning algorithm," in 2018 3rd International Conference on Advanced Robots and Mechatronics (ICARM), 2018: IEEE, pp. 158-164.
J. Peng, Z. Yang, and T. Ma, "Position/force tracking impedance control for robot systems with uncertainties based on adaptive jacobian and neural network," complexity, vol. 2019, p. 1406534.
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Thank You
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