A Computational Multicriteria Optimization Approach to Controller Design for Physical Human-Robot Interaction

Aydın, Yusuf; Tokatlı, Ozan; Patoğlu, Volkan; Başdoğan, Çağatay

doi:10.1109/tro.2020.2998606

Cited by 19 publications

(18 citation statements)

References 51 publications

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“…A general framework based on Pareto optimization is proposed by Aydin et al [132] in order to handle the trade-off between stability robustness and transparency of a closed-loop physical HR interaction. The proposed design not only enables to optimize the controller parameters for the best trade-off performance, but also allows the designer to make a well thought decision by comparing all the interaction optimal controllers with different structures.…”

Section: Efficiency-oriented Control System Designmentioning

confidence: 99%

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario.The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees' safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution.Note to Practitioners-The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

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Section: Efficiency-oriented Control System Designmentioning

confidence: 99%

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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“…Furthermore, our earlier studies [22,46] showed that by setting the fractional order, α, of the admittance controller to a value smaller than 1.0 (see Eq. 2), contact stability can be improved [48]. Hence, in addition to adapting the admittance damping, b, throughout the whole task, the fractional order α was also altered during the Contact phase.…”

Section: Adaptive Control Policymentioning

confidence: 99%

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

Guler¹,

Niaz²,

Madani³

et al. 2022

Preprint

Self Cite

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In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.

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“…In this study, the impedance-filtered method 34 was implemented to set the desired needle position x r ðtÞ…”

Section: Impedance-filtered Amplification Algorithmmentioning

confidence: 99%

An admittance‐controlled amplified force tracking scheme for collaborative lumbar puncture surgical robot system

Nie

Duan

et al. 2022

Robotics Computer Surgery

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Background The accurate sensing and display of the delicate needle‐tissue interaction force to the operator is desirable for needle insertion procedures. It not only plays a significant role in the surgical treatment effect, but also has a great significance in improving surgical safety and reducing the incidence of complications. However, the direct detection of the interaction force between the tissue and needle tip by placement of a force sensor is challenging owing to the constraints of miniaturisation, cost, and sterilisation. Methods In this study, a new position‐based force‐to‐motion controller with magnified force feedback is presented to provide augmented force perception to the operator during needle insertion on the soft tissue. Furthermore, the demonstration to the position‐based low level motion controller is more suitable for needle insertion surgical requirements in the cooperative robotic system. Results The proposed controller was experimentally validated by a collaborative lumbar puncture robotics system. Additionally, to provide hand tremor rejection for the stable manipulation of the puncture needle, it was demonstrated that the proposed amplified feedback force controller allowed a safer object interaction with the robotic needle insertion assistance. Conclusions The results of the experiment show that a desirable interaction force profile is perceived by the operator during the overall insertion task operation. The admittance gain for the simplified admittance controller has a significant impact on the operator's ability to accurately control the applied force.

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A Computational Multicriteria Optimization Approach to Controller Design for Physical Human-Robot Interaction

Cited by 19 publications

References 51 publications

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning

An admittance‐controlled amplified force tracking scheme for collaborative lumbar puncture surgical robot system

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