A Switching Image-Based Visual Servoing Method for Cooperative Continuum Robots

Norouzi-Ghazbi, Somayeh; Janabi–Sharifi, Farrokh

doi:10.1007/s10846-021-01435-w

Cited by 15 publications

(14 citation statements)

References 36 publications

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“…It is challenging to implement highperformance control algorithms in real time when using sophisticated nonlinear models, such as the variable curvature model. Control accuracy can be improved by using electromagnetic sensors [104,105] or analytical calculation [106], visual feedback [107,108], and other feedback techniques. The measured data is used to compensate the model error in the control process and achieve higher control accuracy.…”

Section: Control Methodsmentioning

confidence: 99%

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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In this paper, we describe the advances in the design, actuation, modeling, and control field of continuum robots. After decades of pioneering research, many innovative structural design and actuation methods have arisen. Untethered magnetic robots are a good example; its external actuation characteristic allows for miniaturization, and they have gotten a lot of interest from academics. Furthermore, continuum robots with proprioceptive abilities are also studied. In modeling, modeling approaches based on continuum mechanics and geometric shaping hypothesis have made significant progress after years of research. Geometric exact continuum mechanics yields apparent computing efficiency via discrete modeling when combined with numerical analytic methods such that many effective model-based control methods have been realized. In the control, closed-loop and hybrid control methods offer great accuracy and resilience of motion control when combined with sensor feedback information. On the other hand, the advancement of machine learning has made modeling and control of continuum robots easier. The data-driven modeling technique simplifies modeling and improves anti-interference and generalization abilities. This paper discusses the current development and challenges of continuum robots in the above fields and provides prospects for the future.

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Section: Control Methodsmentioning

confidence: 99%

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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“…Most of these activities cannot be accomplished by a single robot and require team working. [1][2][3][4][5] Painting, assembling, packing, welding, and handling, for instance, all need the engagement of various robot arms with dexterity and high precision. Because of their importance, several model-based control methodologies have arisen for these robots.…”

Section: Introductionmentioning

confidence: 99%

“…These systems are utilized in medical clinics, factory production lines, and service centers because of their exact operation and huge speed. Most of these activities cannot be accomplished by a single robot and require team working 1–5 . Painting, assembling, packing, welding, and handling, for instance, all need the engagement of various robot arms with dexterity and high precision.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Bayat

Izadbakhsh

2023

Intl J Robust & Nonlinear

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This paper presents the position-force tracking control problem of cooperative multiple elastic-joint robotic arms, encountering model nonlinearities, unknown perturbations, and dynamic uncertainties. To this mean, an adaptive function approximation technique (FAT) is proposed, authorizing the reference trajectory to be tracked by the object. This potential originates from the universal approximation feature of the FAT-based methodologies. The (p, q)-analogue of the Bernstein operators is utilized for this purpose. Since the systems' parameters are not precisely known, adaptive laws are employed for adjusting the uncertainty coefficients. The Lyapunov stability theorem assures that all signals in the error space remain uniformly ultimately bounded (UUB). In the end, two elastic-joint arms carrying a rigid object are utilized for validation of the theoretical outcomes. The suggested strategy is also compared to the Chebyshev neural network (CNN). The results show the benefit of the existing method, managing the system even in the presence of disturbances and uncertainties with lower tuning parameters compared with CNN.

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“…Medical clinics, factory production lines, and service centers use robotic systems because of their precise operation and high speed. A single robot cannot complete many of these activities, and a group of UAVs, mobile robots, and manipulators are required to fulfill these tasks [1–5]. Welding, painting, assembling, packing, and handling all require utilization of multiple manipulators precisely.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of cooperative robots in the presence of disturbances and uncertainties: A Bernstein–Chlodowsky approach

Izadbakhsh

Nikdel

Deylami

2023

Math Methods in App Sciences

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The function approximation technique (FAT) is a powerful mathematical tool recently utilized to design model‐free controllers for robots. However, some FAT‐based controllers depend on joint velocities, which may not be available in many real‐world applications. This problem is solved in this paper by proposing an output feedback tracking control for cooperative robotic arms using the Bernstein–Chlodowsky polynomial as an uncertainty approximator. In other words, the Bernstein–Chlodowsky approach is adopted to approximate the lumped uncertainties consisting of disturbances and unmodeled dynamics. An adaptive rule is then suggested to update the approximator's coefficients matrix. Moreover, it is assured that controlled system error signals are uniformly ultimately bounded (UUB) utilizing the Lyapunov lemma. Finally, the designed Bernstein–Chlodowsky controller is applied to a cooperative system with two arms handling a load. Besides, the results of applying the designed technique are compared with the outcomes of the Chebyshev neural network (CNN) as a state‐of‐the‐art approximation method. The simulation outcomes indicate the capability of the designed method.

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A Switching Image-Based Visual Servoing Method for Cooperative Continuum Robots

Cited by 15 publications

References 36 publications

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Adaptive control of cooperative robots in the presence of disturbances and uncertainties: A Bernstein–Chlodowsky approach

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