Design and Calibration of a Joint Torque Sensor for Robot Compliance Control

Bing, Fu; Cai, Ganwei

doi:10.1109/jsen.2021.3104351

Cited by 11 publications

(1 citation statement)

References 26 publications

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“…While failure detection and fault prediction techniques and algorithms in neuro-robots are still emerging, literature discusses failure detection and prediction in traditional robotic controls and manipulators. Several algorithms and methods can accomplish this: second-order sliding mode algorithm (Ferrara and Capisani, 2012), robust non-linear analytic redundancy technique (Halder and Sarkar, 2007), partial least square approach (Muradore and Fiorini, 2012), torque filtering and sensing technique (Fu and Cai, 2021), multiple model adaptive estimation method (Akca and Efe, 2019), multiple hybrid particle swarm optimization algorithm to realize multiple predictions failures (Ayari and Bouamama, 2017), and neural network for prediction of robot execution failures (Diryag et al, 2014). Identifying and understanding failures through the means mentioned are crucial in designing reliable robots that return meaningful explanations to users when and if needed.…”

Section: Advances In Neuro-robotics and Robotic Failures Go Hand In Handmentioning

confidence: 99%

When neuro-robots go wrong: A review

Khan

Olds²

2023

Front. Neurorobot.

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Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union’s Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots’ actions, analogous to an aircraft’s flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.

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Section: Advances In Neuro-robotics and Robotic Failures Go Hand In Handmentioning

confidence: 99%

When neuro-robots go wrong: A review

Khan

Olds²

2023

Front. Neurorobot.

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Dynamic response and chaotic behavior of a controllable flexible robot

et al. 2022

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Flexible robots with controllable mechanisms have advantages over common tandem robots in vibration magnitude, residual vibration time, working speed, and efficiency. However, abnormal vibration can sometimes occur, affecting their operation. Traditionally only simple mechanisms are considered in studying abnormal vibration, omitting reference to important chaotic phenomena caused by the flexibility of the mechanism rod. In order to better understand the causes of abnormal vibration, our work takes a controllable flexible robot with a complex series-parallel mechanism as a research object and uses a combination of Lagrangian and finite element methods to establish its nonlinear elastic dynamics. The effectiveness of the model is verified by comparing the calculated frequency with the frequency measured in a test. The time-domain diagram, phase diagram, Poincaré map, maximum Lyapunov exponent, and bifurcation diagram of the elastic motion of the robot wrist are studied, and the chaotic phenomena in the system are identified through the phase diagram, Poincaré map, the maximum Lyapunov exponent, and the bifurcation diagram. The relationship between the parameters of the robot motion and the maximum Lyapunov exponent is discussed, including trajectory angular speed and radius. The results show that chaotic behavior exists in the controllable flexible robot and that trajectory angular speed and radius all have an influence on the chaotic motion. Our work provides a theoretical basis for further research on the control and optimal design of flexible robot mechanisms.

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