Neuromorphic vision based control for the precise positioning of robotic drilling systems

Ayyad, Abdulla; Halwani, Mohamad; Swart, Dewald; Muthusamy, Rajkumar; Almaskari, Fahad; Zweiri, Yahya

doi:10.1016/j.rcim.2022.102419

Cited by 38 publications

(7 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the separated hardware comprising sensing, storage and processing unit would generate a large amount of redundant data and high power consumption. Inspired by the human vision, neuromorphic visual system with near-/insensor computing architecture has been developed for efficient image perception [43,49,50]. Optoelectronic memristive devices integrate the functionalities of sensing and processing, which present significant potential for application to neuromorphic vision [33,[51][52][53][54].…”

Section: Artificial System For Visual Perceptionmentioning

confidence: 99%

Emerging multimodal memristors for biorealistic neuromorphic applications

Shan,

Lin,

Wang

et al. 2024

Mater. Futures

View full text Add to dashboard Cite

The integration of sensory information from different modalities, such as touch and vision, is essential for organisms to perform behavioral functions such as decision making, learning, and memory. Artificial implementation of human multi-sensory perception using electronic supports is of great significance for achieving efficient human-machine interaction. Thanks to their structural and functional similarity with biological synapses, memristors are emerging as promising nanodevices for developing artificial neuromorphic perception. Memristive devices can sense multidimensional signals including light, pressure, and sound. Their in-sensor computing architecture represents an ideal platform for efficient multimodal perception. We review recent progress in multimodal memristive technology and its application to neuromorphic perception of complex stimuli carrying visual, olfactory, auditory, and tactile information. We describe and clarify the principles underlying memristors and their mechanism of operation. Finally, we discuss the challenges and prospects associated with this rapidly progressing field of research.

show abstract

Section: Artificial System For Visual Perceptionmentioning

confidence: 99%

Emerging multimodal memristors for biorealistic neuromorphic applications

Shan,

Lin,

Wang

et al. 2024

Mater. Futures

View full text Add to dashboard Cite

show abstract

“…The pixel-wise intensity changes, called events or spikes, are recorded at a temporal resolution on the order of microseconds. Event-based cameras have been applied in autonomous drone racing [38], space imaging [39], space exploration [40], automated drilling [41], and visual servoing [42,43]. Neuromorphic cameras' fast update rate, along with their high dynamic range (140 dB compared to conventional cameras with 60 dB [44]) and low power consumption, make them apt for robotics tasks [45].…”

Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

show abstract

“…These motors, while presenting opportunities for enhanced drilling and increased penetration rates, also face challenges such as wear, temperature sensitivity, and power constraints [6,7]. High-speed drilling is a vital part of the oil and gas industry, but it puts critical components like thrust pins and inserts, which are essential components of the mud motor's transmission section (Figure 1), under extreme mechanical stress and wear [8]. While numerous studies have investigated the general wear mechanisms in drilling operations, there remains a limited understanding of the wear behavior specific to thrust pins and inserts, (Figure 2), in high-speed drilling motors.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wear Resistance of Drilling Motor Components: A Tribological and Materials Application Study

Benarbia,

Tomomewo,

Laalam

et al. 2024

Eng

View full text Add to dashboard Cite

The oil and gas industry faces significant challenges due to wear on drilling motor components, such as thrust pins and inserts. These components are critical to the efficiency and reliability of drilling operations, yet are susceptible to wear, leading to significant economic losses, operational downtime, and safety risks. Despite previous research on wear-resistant materials and surface treatments, gaps exist in understanding the unique properties of thrust pins and inserts. The aim of this study is to enhance mechanical system performance by characterizing the wear resistance of these components. Through chemical analysis, hardness assessments, and metallographic examinations, the study seeks to identify specific alloys and microstructures conducive to wear resistance. Key findings reveal that AISI 9314 thrust pins exhibit superior wear resistance with a tempered martensite microstructure and a hardness of 41 HRc, whereas AISI 9310 inserts are less resistant, with a hardness of 35 HRc. The research employs advanced techniques, including a pin-on-disc tribometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and profilometry, to evaluate wear behavior, visualize wear patterns, analyze elemental composition, and quantify material loss and surface roughness. Our findings demonstrate that optimizing the material selection can significantly enhance the durability and efficiency of drilling motors. This has profound implications for the oil and gas industry, offering pathways to reduce maintenance costs, improve operational efficiency, and contribute to environmental sustainability by optimizing energy consumption and minimizing the carbon footprint of drilling operations.

show abstract

Neuromorphic vision based control for the precise positioning of robotic drilling systems

Cited by 38 publications

References 51 publications

Emerging multimodal memristors for biorealistic neuromorphic applications

Emerging multimodal memristors for biorealistic neuromorphic applications

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Enhancing Wear Resistance of Drilling Motor Components: A Tribological and Materials Application Study

Contact Info

Product

Resources

About