Understanding the Implementation of Impedance Control in Industrial Robots

Lahr, Gustavo J. G.; Soares, Joao V.R.; Garcia, Henrique B.; Siqueira, Adriano A. G.; Caurin, Glauco Augusto de Paula

doi:10.1109/lars-sbr.2016.52

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…On the other hand, compliance control strategies can be classified into two categories: passive compliance and active compliance [ 36 , 37 ]. In this research area, most compliance control strategies are based on passive compliance [ 17 , 31 , 38 ], which may lead to insufficient stiffness and cause a lower polishing efficiency. Usually, the impedance control strategy is used in the robotic control research area, especially for robot–human collaboration applications [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Li,

Cheung,

Lam

et al. 2024

Sensors

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Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

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Section: Introductionmentioning

confidence: 99%

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Li,

Cheung,

Lam

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Approaches that require iterative contact with the target object must also consider the contact dynamics and control approach. Impedance control is frequently used in manufacturing and relies on force feedback to determine proper contact [18]. Due to the success of impedance controllers for manufacturing processes and operations that include adaptation to force, impedance controllers have been applied to space operations.…”

Section: Introductionmentioning

confidence: 99%

Space Manipulator Collision Avoidance Using a Deep Reinforcement Learning Control

Blaise,

Bazzocchi

2023

Aerospace

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Recent efforts in on-orbit servicing, manufacturing, and debris removal have accentuated some of the challenges related to close-proximity space manipulation. Orbital debris threatens future space endeavors driving active removal missions. Additionally, refueling missions have become increasingly viable to prolong satellite life and mitigate future debris generation. The ability to capture cooperative and non-cooperative spacecraft is an essential step for refueling or removal missions. In close-proximity capture, collision avoidance remains a challenge during trajectory planning for space manipulators. In this research, a deep reinforcement learning control approach is applied to a three-degrees-of-freedom manipulator to capture space objects and avoid collisions. This approach is investigated in both free-flying and free-floating scenarios, where the target object is either cooperative or non-cooperative. A deep reinforcement learning controller is trained for each scenario to effectively reach a target capture location on a simulated spacecraft model while avoiding collisions. Collisions between the base spacecraft and the target spacecraft are avoided in the planned manipulator trajectories. The trained model is tested for each scenario and the results for the manipulator and base motion are detailed and discussed.

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Admittance Control Study Applied in the Exercise of Proprioception

Lacerda

Arthur

2022

Proceedings of the 7th Brazilian Technology Symposium (BTSym’21)

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Understanding the Implementation of Impedance Control in Industrial Robots

Cited by 8 publications

References 10 publications

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing

Space Manipulator Collision Avoidance Using a Deep Reinforcement Learning Control

Admittance Control Study Applied in the Exercise of Proprioception

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