First high speed simultaneous force feedback for multi-trap optical tweezers

Ni, Zhenjiang; Yin, Munan; Pacoret, Cécile; Benosman, Ryad; Régnier, Stéphane

doi:10.1109/aim.2014.6878038

Cited by 4 publications

(7 citation statements)

References 21 publications

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“…The human operator controls the OT using the master haptic interface, and the pico-newton forces detected by a vision system are provided to the operator through the same haptic device. Ni et al (2014) recently extended this approach to multitrap OT. Mehrtash et al (2012) developed a haptic-enabled virtual reality interface for a magnetic-haptic micromanipulation platform.…”

Section: Atomic Force Microscopymentioning

confidence: 99%

Haptic Feedback for Microrobotics Applications: A Review

et al. 2016

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Microrobotics systems are showing promising results in several applications and scenarios, such as targeted drug delivery and screening, biopsy, environmental control, surgery, and assembly. While most of the systems presented in the literature consider autonomous techniques, there is a growing interest in human-in-the-loop approaches. For reasons of responsibility, safety, and public acceptance, it is in fact beneficial to provide a human with intuitive and effective means for directly controlling these microrobotic systems. In this respect, haptic feedback is widely believed to be a valuable tool in human-in-the-loop teleoperation systems. This article presents a review of the literature on haptic feedback systems for microrobotics, categorizing it according to the type of haptic technology employed. In particular, we considered both tethered and untethered systems, including applications of micropositioning, microassembly, minimally invasive surgery, delivery of objects, micromanipulation, and injection of cells. One of the main challenges for an effective implementation is stability control. In fact, the high scaling factors introduced to match variables in the macro and the micro worlds may introduce instabilities. Another challenge lies in the measurement of position and force signals in the remote environment. The integration of microsized sensors may significantly increase the complexity and cost of tools fabrication. To overcome the lack of force-sensing, vision seems a promising solution. Finally, although the literature on haptic feedback for untethered microrobotics is still quite small, we foreseen a great development of this field of research, thanks to its flexible applications in biomedical engineering scenarios.

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Section: Atomic Force Microscopymentioning

confidence: 99%

Haptic Feedback for Microrobotics Applications: A Review

et al. 2016

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“…This is important for decision making and other cognitive functions. The many studies conducted with the DVS and similar event-based visual devices have covered a variety of tasks ranging from classification and recognition [6-8, 11, 14, 21, 24, 29, 31-33, 39, 41-46], tracking [10, 16-18, 22, 23, 26, 28, 30, 37, 38, 40, 47-50], stereo matching and depth reconstruction [19,[51][52][53][54], robotic applications [9,12,13,16,17,20,25,27,30,37,38,47,48,[55][56][57], and others [15,36,[58][59][60][61][62][63][64]. The prediction task, however, remains infrequently researched [63,64].…”

Section: Spiking Neural Network With Long-term Predictionmentioning

confidence: 99%

“…As the DVS is built for mobile robotic use, there are many studies that utilise the DVS with some robotic platform [12,13,16,17,25,30,37,38,47,48,55,56]. Robotic studies usually first recognise or track certain objects, and then control the robot motor action accordingly to accomplish their goals.…”

Section: Previous Tasks Using Event-based Camerasmentioning

confidence: 99%

“…• Tracking the position of a probe which then guides an optic tweezer for micro-operation [16,30,47,48] • Tracking a moving robot [10] • Tracking the position of a pencil and balancing it on its tip [17] • Multiple microsphere tracking [18] • Ball movement tracking which drives a robotic goalie arm [37,38] • Part-based shape tracking [49] • Fingertip tracking for a touchless user interface [50] • Tracking particles [26] • Tracking lane changing cars [22] • Tracking shapes [23] • Tracking movement of multiple people [40] • Tracking features and edges [28] Stereo matching and depth reconstruction…”

Section: Trackingmentioning

confidence: 99%

“…• Probe position tracking and guiding a pair of robotic optic tweezer for micro-operation [16,30,47,48] • Tracking the position of a pencil and balancing it on its tips [17] • Tracking a ball's movement with a robotic goalie arm and blocking it accordingly [37,38] • Open loop control of a robot based on DVS inputs [25,55] • Visual attention [12,13] • Generate visual odometry [56] Continued on next page • Using particle filtering to track a pre-marked line of a ceiling for self-localisation [20] and an extended SLAM version [27] • Self-localisation of a flying robot with active LED marking tracking [57] • Localisation of micro aerial vehicles [9] Others…”

Section: Trackingmentioning

confidence: 99%

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Inspired by nature: timescale-free and grid-free event-based computing with spiking neural networks

Gibson¹

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Conception and design (50%) Analysis and interpretation (70%) Drafting and production (70%) Author Scott Heath Conception and design (2%) Analysis and interpretation (10%) Drafting and production (2%) Author Robert P. Quinn Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author Alexia H. Lee Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author Joshua T. Arnold Conception and design (3%) Analysis and interpretation (10%) Drafting and production (2%) Author Tharun S. Sonti Conception and design (3%) Analysis and interpretation (10%) Drafting and production (2%) Author Andrew Whalley Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author George P. Shannon Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author Brian T. Song Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author James A. Henderson Conception and design (2%) Analysis and interpretation (0%) Drafting and production (2%) Author Janet Wiles Conception and design (30%) Analysis and interpretation (0%) Drafting and production (12%) 7 [2] Tingting (Amy) Gibson, James A. Henderson, and Janet Wiles, "Predicting temporal sequences using an event-based spiking neural network incorporating learnable delays," in Neural Networks (IJCNN), 2014 International Joint Conference on. IEEE, 2014, pp. 3213-3220.-incorporated as Chapter 4 Contributor Statement of contribution Author Tingting (Amy) Gibson (Candidate) Conception and design (70%) Analysis and interpretation (80%) Drafting and production (80%) Author James A. Henderson Conception and design (0%) Analysis and interpretation (0%) Drafting and production (10%) Author Janet Wiles Conception and design (30%) Analysis and interpretation (20%) Drafting and production (10%) List of Abbreviations DVS Dynamic vision sensor ANN Artificial neural network SNN Spiking neural network AER Address-event representation iRat Intelligent Rat animat Technology MNIST Modified National Institute of Standards and Technology CIS Complex and Intelligent Systems (CIS is now part of the Co-Innovation group, CIN) D D D KL Kullback-Leibler divergence 1. Introduction to timescale-free and grid-free events with bio-inspired spiking neural networks 1. Introduction to timescale-free and grid-free events with bio-inspired spiking neural networks

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