Kinect-based Computed Torque Control for lynxmotion robotic arm

Benabdallah, Ismail; Bouteraa, Yassine; Boucetta, Rahma; Rekik, Chokri

doi:10.1109/icmic.2015.7409416

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…A modified human-machine-interface (HMI) has been developed to interact with telemanipulation system. This HMI is presented in our previous work (Benabdallah et al , 2015; Ben Abdallah et al , 2016), containing many options to establish a perfect and high-friendly human–robot interaction. These characteristics can be abstracted in few points such as hardware configuration, experimental recording and experimental results.…”

Section: Resultsmentioning

confidence: 99%

“…Kinect is a motion sensor that provides a natural user interface available for several applications in different fields including game-based learning systems (Tsai et al , 2015; Chuang et al , 2014), stroke rehabilitation (Bower et al , 2015; Štrbac et al , 2014), helping visually impaired people (Kanwal et al , 2015; Pham et al , 2016), navigation systems (Tuvshinjargal et al , 2015; Xu et al , 2015) and other fields (Kim and Kim, 2015; Aragón et al , 2013). Based on its internal processor, Kinect sensor can recognize human movement patterns to generate a corresponding skeleton coordinates that can be provided in a computer environment such as Matlab (Brecher et al , 2012; Li, 2013), LabVIEW (Muhiddin et al , 2013; Benabdallah et al , 2015) and .NET environment (Lee et al , 2015). Therefore, the dynamic gesture recognition technology has gained increased attention.…”

Section: Introductionmentioning

confidence: 99%

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A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

Bouteraa

Abdallah

2017

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Purpose The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution. Design/methodology/approach The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-based approach for hand posture recognition cannot be the suitable solution mainly when the hand is occluded in such situations. The proposed approach supports the vision-based system by an electromyography (EMG)-based biofeedback system for posture recognition. Moreover, the novel approach appends to the vision system-based gesture control and the EMG-based posture recognition a force feedback to inform operator of the real grasping state. Findings The main finding is to have a robust method able to gesture-based control a robot manipulator during movement, manipulation and grasp. The proposed approach uses a real-time gesture control technique based on a kinect camera that can provide the exact position of each joint of the operator’s arm. The developed solution integrates also an EMG biofeedback and a force feedback in its control loop. In addition, the authors propose a high-friendly human-machine-interface (HMI) which allows user to control in real time a robotic arm. Robust trajectory tracking challenge has been solved by the implementation of the sliding mode controller. A fuzzy logic controller has been implemented to manage the grasping task based on the EMG signal. Experimental results have shown a high efficiency of the proposed approach. Research limitations/implications There are some constraints when applying the proposed method, such as the sensibility of the desired trajectory generated by the human arm even in case of random and unwanted movements. This can damage the manipulated object during the teleoperation process. In this case, such operator skills are highly required. Practical implications The developed control approach can be used in all applications, which require real-time human robot cooperation. Originality/value The main advantage of the developed approach is that it benefits at the same time of three various techniques: EMG biofeedback, vision-based system and haptic feedback. In such situation, using only vision-based approaches mainly for the hand postures recognition is not effective. Therefore, the recognition should be based on the biofeedback naturally generated by the muscles responsible of each posture. Moreover, the use of force sensor in closed-loop control scheme without operator intervention is ineffective in the special cases in which the manipulated objects vary in a wide range with different metallic characteristics. Therefore, the use of human-in-the-loop technique can imitate the natural human postures in the grasping task.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

Bouteraa

Abdallah

2017

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“…On the other hand, the Kinect sensor has shown elegant capabilities making it an excellent option to consider in any vision-based applications. Examples of these applications are assistive systems [24,38], rehabilitation [1,2,8,9,42], and navigation systems [46,50]. Also, Kinect has been seen in many gesture recognition approaches [10,11,17,23].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Bouteraa

Abdallah

Elmogy

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion /extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient’s safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients’ conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system.

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“…To control the robot, the Forward And Backward Reaching Inverse Kinematics (FABRIK) method [8] can be used to avoid singularities and speed up calculation, while achieving good performance in the tracking of the target by the end-effector. In [9][10][11], a Kinect-based solution controls the end-effector, but does not consider the robotic arm posture. A markerless approach offers simplicity, low cost to build, and a less physically cumbersome user experience.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Posture Control for a Robotic Manipulator Using Natural Human–Computer Interaction

Plouffe

Payeur

Creţu

2018

5th International Electronic Conference on Sensors and Applications

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In this paper, we propose a vision-based recognition approach to control the posture of a robotic arm with three degrees of freedom (DOF) using static and dynamic human hand gestures. Two different methods are investigated to intuitively control a robotic arm posture in real-time using depth data collected by a Kinect sensor. In the first method, the user’s right index fingertip position is mapped to compute the inverse kinematics on the robot. Using the Forward And Backward Reaching Inverse Kinematics (FABRIK) algorithm, the inverse kinematics (IK) solutions are displayed in a graphical interface. Using this interface and his left hand, the user can intuitively browse and select a desired robotic arm posture. In the second method, the user’s left index position and direction are respectively used to determine the end-effector position and an attraction point position. The latter enables the control of the robotic arm posture. The performance of these real-time natural human control approaches is evaluated for precision and speed against static and dynamic obstacles.

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Kinect-based Computed Torque Control for lynxmotion robotic arm

Cited by 13 publications

References 6 publications

A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Real-Time Posture Control for a Robotic Manipulator Using Natural Human–Computer Interaction

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