3D Assembly Group Analysis for Cognitive Automation

Brecher, Christian; Breitbach, Thomas; Müller, Simon; Mayer, Monique N.; Odenthal, Barbara; Schlick, Christopher; Herfs, Werner

doi:10.1155/2012/375642

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

Bouteraa

Abdallah

2017

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“…A concept that allows the cognitive automation of robotic assembly processes is presented in [9]. To verify the concept, an assembly cell comprised of two robots was designed.…”

Section: Introductionmentioning

confidence: 99%

Genetic Re-planning Strategy of Wormhole Model using Neural Learned Vibration Behavior in Robotic Assembly

Banjanović-Mehmedović

Mehmedović²,

Bosankic³

et al. 2013

Automatika

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Original scientific paperThis paper investigates the genetic based re-planning search strategy, using neural learned vibration behavior for achieving tolerance compensation of uncertainties in robotic assembly. The vibration behavior was created from complex robot assembly of cogged tube over multistage planetary speed. Complex extensive experimental investigations were conducted for the purpose of finding the optimum vibration solution for each planetary stage reducer in order to complete the assembly process in defined real-time. However, tuning those parameters through experimental discovering for improved performance is a time consuming process. Neural network based learning was used to generate wider scope of parameters in order to improve the robot behavior during each state of the assembly process. As a novel modelling formalism of reactive hybrid automata, we propose the Wormhole Model with both learning and re-planning capacities (WOMOLERE). For our application, the states of hybrid automaton include amplitudes and frequencies of robot vibration module. The transition action is a function of minimal distance and uncertainty effects due to jamming during the assembly process. The results suggest that the methodology is adequate and could be recognized as an idea for designing of robot surgery assistance methods, especially in soft-robotics. Genetski bazirana strategija re-planiranja modela crvotočine korištenjem neuronski naučenog vibracijskog ponašanja u robotskoj montaži. Ovajčlanak prezentira strategiju re-planiranja kretanja, baziranu na genetskom algoritmu, korištenjem neuronski naučenog vibracijskog ponašanja u cilju postizanja kompenzacije tolerancije neizvjesnosti u procesu robotske montaže. Vibracijsko ponašanje je kreirano iz kompleksne robotske montaže nazubljene cjevčice preko višestupanjskog planetarnog reduktora brzine motora. Provedeni su brojni eksperimenti s ciljem odreîivanja optimalnih amplituda i frekvencija vibracijskog modula robota za svaki stupanj reduktora s ciljem završetka procesa montaže u definiranom realnom vremenu. Meîutim, podešavanje ovih parametara kroz eksperimente u cilju unaprjeîenja perfomansi je vremenski zahtjevan proces. Učenje bazirano na neuronskim mrežama je korišteno za generiranje šireg opsega parametara stanja modela u cilju unaprjeîenja robotskog ponašanja tijekom svake faze procesa montiranja. Kao novi formalizam modeliranja reaktivnog hibridnog automata, predložili smo model crvotočine, sa sposobnostima i učenja i re-planiranja (WOMOLERE). Za našu aplikaciju, stanja hibridnog automata sadrže amplitude i frekvencije robotskog vibracijskog modula. Akcija tranzicije je funkcija minimalne distance i učinaka neizvjesnosti uslijed zaglavljenja pri procesu montiranja. Rezultati pokazuju da je metodologija prikladna i kao ideja se može koristiti u dizajniranju metoda robotske asistencije pri operacijama, osobito u soft robotici.

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