Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

Su, Hang; Hu, Yingbai; Karimi, Hamid Reza; Knoll, Alois; Ferrigno, Giancarlo; Momi, Elena De

doi:10.1016/j.neunet.2020.07.033

Cited by 177 publications

(102 citation statements)

References 24 publications

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“…Belhaj and Tagina (2009) apply RNNs to model and predict internet end-to-end time delay. Su et al (2020a) propose an improved RNN to predict the trajectory of manipulators. Aburime et al (2019) applied recursive least squares filtering to identify the delay and target waypoints.…”

Section: Examples Of Time Series Prediction In Delay Mitigationmentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

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Section: Examples Of Time Series Prediction In Delay Mitigationmentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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“…The use of Artificial Intelligence also helps improving co-manipulation applications, adding mechanisms to tune and optimize control models. Su et al [9] propose the use of a recurrent neural network (RNN) to perform the trajectory control of redundant robot manipulators. Roveda et.…”

Section: Related Workmentioning

confidence: 99%

Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation

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et al. 2020

Sensors

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The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.

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“…There are different learning approaches which differ from each other by the way of adjusting the weights, and their structure depends on the architecture of the neural network and the task to be performed. Besides, neural networks have been searched and carried out in real systems [8,9]; there are many ANN applications in data analysis, identification and model control [10]. Amid various types of ANN, a MLP is quite popular and used extensively in research.…”

Section: Introductionmentioning

confidence: 99%

Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

et al. 2021

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This work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.

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Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

Cited by 177 publications

References 24 publications

A Brief Survey of Telerobotic Time Delay Mitigation

A Brief Survey of Telerobotic Time Delay Mitigation

Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation

Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

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