Robotic Arm Control with Hand Movement Gestures

Bularka, Szilard; Szabó, Roland; Oteşteanu, Marius; Băbăiţă, Mircea

doi:10.1109/tsp.2018.8441341

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…The Finger Gesture Recognition System utilizes active tracking mechanisms to facilitate interaction with portable devices or computers through finger gestures, with speech as an alternative mode. This system can integrate into Perceptual User Interfaces (PUIs) for applications in Virtual Reality or Augmented Reality environments [2].…”

Section: Literature Surveymentioning

confidence: 99%

Design and Development of Gesture Controlled Robotic Arm

2024

International Journal of Science and Engineering Applications

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In the contemporary landscape, robotic hands are pivotal across military, defense, medical, and industrial sectors, replicating human hand gestures for tasks even in hazardous environments like firework manufacturing or bomb diffusion. This paper introduces a groundbreaking approach to wirelessly control robotic hand movements via gesture recognition. By employing servo control, flex sensors, Arduino Nano, and transceivers, predefined gestures captured by the transceiver enable seamless real-time communication between users and the robotic hand, facilitating remote operation. This innovative integration of gesture recognition technology and robotics presents an exciting frontier for enhancing human-machine interaction, offering adaptability and safety across a myriad of applications.I.

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Section: Literature Surveymentioning

confidence: 99%

Design and Development of Gesture Controlled Robotic Arm

2024

International Journal of Science and Engineering Applications

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“…Monocular ranging has the advantages of simple calibration, convenient installation, and high economic efciency. Te three-dimensional coordinates of the target object in the coordinate system of the fve-axis redundant industrial manipulator base are obtained to achieve target positioning [6][7][8].…”

Section: Object Localization Based On Monocular Visionmentioning

confidence: 99%

Coordinated Compliance Control Method of Five-Axis Redundant Industrial Manipulator Based on Monocular Vision

Cen¹

2022

Journal of Control Science and Engineering

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In order to improve the accuracy of the five-axis redundant industrial robot arm in grasping static objects and shorten the grasping time, a coordinated compliance control method based on a monocular vision for the five-axis redundant industrial robot arm is proposed in this paper. Using the monocular vision ranging method, the three-dimensional coordinates of the target object in a base coordinate system of the five-axis redundant industrial robotic arm are calculated and object target positioning is achieved. According to the acquired object target position, the traditional Euler angle is used to calculate the actuator posture impedance at the end of the robotic arm, thereby realizing the coordinated compliant control of the five-axis redundant industrial manipulator. The simulation experiment results show that the proposed coordinated compliance control method for a five-axis redundant industrial manipulator based on monocular vision can successfully grasp the target object in the shortest time and has high practical value.

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“…Motion sensors, including accelerometers, play a crucial role in enabling precise human motion capture for mimicking robots, which can be easily interfaced with microcontrollers. They excel at measuring tilt in the x and y planes, converting the data into an analog signal [4] [5]. For capturing hand motion, researchers use data gloves equipped with flex sensors which accurately recognize specific finger movements [6] [7].…”

Section: Introductionmentioning

confidence: 99%

PALMitate: Hand Gesture Robotic Hand Emulator

Cuñado,

Gertos,

Jimenez

et al. 2023

Advances in Transdisciplinary Engineering

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Safety-critical situations inevitably require human intervention, thereby, exposing personnel to formidable risks. This encompasses various domains, such as bomb disposal, emergency response, and hazardous material handling, all underlining the paramount importance of personnel safety. This research is dedicated to the development of a wireless robotic hand that replicates predefined gestures, facilitated by image processing algorithms. The hand gesture classifier attains an accuracy rate of 94.67% in identifying and distinguishing gestures. The implementation of hand gesture robotic hand emulators in safety-critical scenarios emerges as a pivotal strategy to potentially replace human participation. This substitution substantially mitigates risks for personnel. Moreover, it stands to enhance safety standards by virtue of its automated and controlled operation, thus minimizing the vulnerability associated with manual intervention. Fundamentally, this study acts as empirical validation of the substantial transformative capabilities inherent in robotics. This has the potential to completely reshape how dangerous tasks are handled in various industries which has a direct impact on keeping people safe and protecting valuable lives. Even though this research investigates a constrained selection of hand gestures and operates within a time limit for emulation, it establishes the fundamental basis for future progress in human-robot interaction and the realm of gesture recognition.

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Robotic Arm Control with Hand Movement Gestures

Cited by 11 publications

References 8 publications

Design and Development of Gesture Controlled Robotic Arm

Design and Development of Gesture Controlled Robotic Arm

Coordinated Compliance Control Method of Five-Axis Redundant Industrial Manipulator Based on Monocular Vision

PALMitate: Hand Gesture Robotic Hand Emulator

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