LabVIEW based four DoF robotic ARM

Pol, Rahul Shivaji; Giri, Sagar; Ravishankar, Aditya; Ghode, Varsha

doi:10.1109/icacci.2016.7732308

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…In the context of physical computing, research work in [134] [135] [136] supports the design of educational activities by using LabVIEW that has a number of tools for data collection, processing and measurements graphical display. By using LabVIEW, a teacher can design and develop custom control systems for robotic mechanisms, due to the easiness offered by the interface of Maker Hub with the Arduino platform [138]. Fig.…”

Section: Educational Technologies Applications a Physical Natural Sci...mentioning

confidence: 99%

Digital Tools, Technologies, and Learning Methodologies for Education 4.0 Frameworks: A STEM Oriented Survey

Boltsi,

Kalovrektis,

Xenakis

et al. 2024

IEEE Access

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The correlation between learning and teaching technologies is multidimensional and complex, thus, the digital tools that can help students turning information into knowledge define the crucial role of pedagogical practice. Open access to hardware and software tools is a focal point for advancing learning. In sciences, lab-based experiments constitute a vital part of curricula that bridge the gap between theory and practice. Access to remote lab tools is possible by pairing simulators, emulators and actual equipment, located in various geographical locations. Under this premise, Education 4.0 defines a new learning framework aligned with the fourth industrial revolution digital requirements. These constitute smart sensors, Artificial Intelligence (AI) and Robotics. In this context, University graduates need to prepare themselves with complex problem solving and interdisciplinary problems. To this end, contemporary Science, Technology, Engineering and Mathematics (STEM) teaching practices should be present along the way. Digitally competent students need to learn from a modern and adjustable curriculum tailored to cover necessary skills related to Internet of Things (IoT). Therefore, the digitization of University campuses, including smart IoT, Information and Communication Technology (ICT) and Fifth Generation (5G) automation infrastructures is necessary. In this work, we carry out an extensive STEM oriented survey which highlights and categorizes all contemporary digital tools and technologies needed to support future graduates. Moreover, we focus on contemporary pedagogical and didactic approaches to support Education 4.0 skills. We match contemporary methods with lab types, expected learning outcomes and open software and hardware tools, as imposed by the Education 4.0 framework. Our scenarios mainly focus on University education and consider smart sensors, IoT, 5G technologies, which are the basic building components for students' digital competencies to meet Education 4.0 requirements.

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Section: Educational Technologies Applications a Physical Natural Sci...mentioning

confidence: 99%

Digital Tools, Technologies, and Learning Methodologies for Education 4.0 Frameworks: A STEM Oriented Survey

Boltsi,

Kalovrektis,

Xenakis

et al. 2024

IEEE Access

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“…M. J. Ansari et al [8] study and develop microcontroller-based Arm robot control system. Arm robot programming in LabVIEW has been done in previous study by R. S. Pol et al [9].…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Ramadhan

Lilansa

Rifa'i

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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Most arm robot has an inefficient operating time because it requires operator to input destination coordinates. Besides, main problem of arm robot is object’s vulnerability when it is manipulated by the robot. This research goals is to develop an arm robot control system which has ability to automatically detect object using image processing in order to reduce operating time. It is also able to control gripping force for eliminating damage to objects caused by robot gripper. This research is implemented in LabVIEW 2011 software to control arm robot model which can represent industrial scale robot. The software is designed with informative visualization to help user learn and understand robotic control concept deeply. The system can automatically detect object position based on pattern recognition method which has four steps: pre-processing process to initialize picture taken by camera, segmentation process for separating object from the background, classification process to determine characteristics of object, and position estimation process to estimate object position in the picture. The object’s position data are then calculated by using kinematic equation to control the robot’s motion. The results show that the system is able to detect object and move the robot automatically with accuracy rate in x-axis is 95.578 % and in y-axis is 92.878 %. The system also implements modified PI control method with FSR as input to control gripping force with maximum overshoot value 10 %. Arm robot model control system developed is successfully meet the expectation. The system control can be implemented to industrial scale arm robot with several modification because of kinematic similarity between model and industrial scale robot.

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