Adaptive-Constrained Impedance Control for Human–Robot Co-Transportation

Yu, Xinbo; Li, Bin; He, Wei; Feng, Yanghe; Cheng, Long; Silvestre, Carlos

doi:10.1109/tcyb.2021.3107357

Cited by 108 publications

(41 citation statements)

References 63 publications

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“…The most common contactless devices in teleoperation are low-cost RGB cameras. Making use of human body tracking and hand pose estimation algorithms, markerless vision-based teleoperation has been studied in controlling humanoid robots or dexterous robotic hands [24]. Many works separately research the visual perception of human bodies (e.g., human gesture classification or human hand pose estimation) and robot control (e.g., specific motions or kinematic retargeting) [25], [26].…”

Section: A Robotic Teleoperationmentioning

confidence: 99%

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision

Li¹,

Hendrich²,

Liang³

et al. 2024

IEEE Trans. Cybern.

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Markerless vision-based teleoperation that leverages innovations in computer vision offers the advantages of allowing natural and noninvasive finger motions for multifingered robot hands. However, current pose estimation methods still face inaccuracy issues due to the self-occlusion of the fingers. Herein, we develop a novel vision-based hand-arm teleoperation system that captures the human hands from the best viewpoint and at a suitable distance. This teleoperation system consists of an end-toend hand pose regression network and a controlled active vision system. The end-to-end pose regression network (Transteleop), combined with an auxiliary reconstruction loss function, captures the human hand through a low-cost depth camera and predicts joint commands of the robot based on the image-toimage translation method. To obtain the optimal observation of the human hand, an active vision system is implemented by a robot arm at the local site that ensures the high accuracy of the proposed neural network. Human arm motions are simultaneously mapped to the slave robot arm under relative control. Quantitative network evaluation and a variety of complex manipulation tasks, for example, tower building, pouring, and multitable cup stacking, demonstrate the practicality and stability of the proposed teleoperation system.

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Section: A Robotic Teleoperationmentioning

confidence: 99%

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision

Li¹,

Hendrich²,

Liang³

et al. 2024

IEEE Trans. Cybern.

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“…According to the patient's condition and different rehabilitation training stages, different rehabilitation training modes need to be adopted, and corresponding control methods should also be used (Table 1 passive control mode is aimed at patients with severe diseases and weak muscle strength; here, the affected limbs are driven by the robot to move along a predetermined trajectory. From the perspective of robot control, robots perform trajectory tracking tasks in passive training, which can be achieved through trajectory tracking control methods, such as proportional−derivative (PD) control [79], computational torque control [62], variable structure control [80] and impedance control [81]. The controller mentioned above does not take humans into account; that is, the trajectory tracking control realised focuses on the movement of the robot and the tracking of expected motion.…”

Section: Human-robot Coordinate Control Strategiesmentioning

confidence: 99%

“…At this time, the trajectory is no longer pre-set, but can be obtained by real-time reference changes [97,98]. A finite-state machine defines different motion Passive control Proportional−derivative (PD) control [79], computational torque control [62], variable structure control [80], impedance control [81], multiple input multiple output (MIMO) decoupling control [51] After a walk mode based on the sensors was selected, the participant initiated and propagated the programmed motions. The torque that the robot needs to apply to the human body is generally put into the dynamics equation as a disturbance term Assist-as-needed control Force-field control (FFC) [5], moment-field control (MFC) [82], three-dimensional-force-field control (3D-FFC) [83] Using physical sensors for measurement and evaluation; that is, the actual position or attitude deviation measured by the sensor can obtain the corresponding adjustment force/torque to achieve impedance control based on the attitude deviation Neuromuscular control [57] Capturing EMG signals to generate a synchronised and natural gait and achieve human-robot coordinated control Force control Finite state machine [84] A finite state machine is used to indicate the intended option of a series of manoeuvres.…”

Section: Human-robot Coordinate Control Strategiesmentioning

confidence: 99%

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Review of human—robot coordination control for rehabilitation based on motor function evaluation

et al. 2022

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As a wearable and intelligent system, a lower limb exoskeleton rehabilitation robot can provide auxiliary rehabilitation training for patients with lower limb walking impairment/loss and address the existing problem of insufficient medical resources. One of the main elements of such a human—robot coupling system is a control system to ensure human—robot coordination. This review aims to summarise the development of human—robot coordination control and the associated research achievements and provide insight into the research challenges in promoting innovative design in such control systems. The patients’ functional disorders and clinical rehabilitation needs regarding lower limbs are analysed in detail, forming the basis for the human—robot coordination of lower limb rehabilitation robots. Then, human—robot coordination is discussed in terms of three aspects: modelling, perception and control. Based on the reviewed research, the demand for robotic rehabilitation, modelling for human—robot coupling systems with new structures and assessment methods with different etiologies based on multi-mode sensors are discussed in detail, suggesting development directions of human—robot coordination and providing a reference for relevant research.

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“…In this case, joint angular variables of fault joints may not be directly involved in the redundancy resolution without any additional constraints. It is different from the way of processing in good working status for redundant robots, that is, motion planning and control of end-effectors in the workspace can be done through directly seeking control actions of all the joints in the way of redundancy resolution [6,7,8], and motion planning and control of redundant robots in normal working conditions have already achieved great success [9] with many efficient approaches have been developed under different scenarios [10,11]. Some academics have developed fault-tolerant analysis and motion planning methodologies in the past several years in order to find realistic solutions for redundant robots to overcome the difficulties of generalizing analytical solutions from Cartesian space to joint space.…”

Section: Introductionmentioning

confidence: 99%

A sparsity-based method for fault-tolerant manipulation of a redundant robot

et al. 2022

Robotica

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As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

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Adaptive-Constrained Impedance Control for Human–Robot Co-Transportation

Cited by 108 publications

References 63 publications

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision

A Dexterous Hand-Arm Teleoperation System Based on Hand Pose Estimation and Active Vision

Review of human—robot coordination control for rehabilitation based on motor function evaluation

A sparsity-based method for fault-tolerant manipulation of a redundant robot

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