Robotics in the Small, Part I: Microbotics

Abbott, Jake J.; Nagy, Zoltán; Beyeler, Felix; Nelson, Bradley J.

doi:10.1109/mra.2007.380641

Cited by 311 publications

(129 citation statements)

References 29 publications

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“…This value of Re is quite higher than the typical values (Re < 10) present at smaller scales [17], in which the linear drag Stokes' law is usually applied. In our case, this high Reynolds number indicates that the drag force takes a quadratic form:…”

Section: Motion Modelmentioning

confidence: 68%

A trajectory-based calibration method for stochastic motion models

Mario

Mermoud

Mastrangeli

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consists in minimizing the Kolmogorov-Smirnov (KS) distance between the step length and step angle distributions of real and simulated trajectories generated by the robots. First, we validate this method by calibrating a physics-based motion model of a single 3-cm-sized robot floating at a water/air interface under fluidic agitation. Second, we extend the focus of our work to multi-robot systems by performing a sensitivity analysis of our stochastic motion model in the context of SelfAssembly (SA). In particular, we compare in simulation the effect of perturbing the calibrated parameters on the predicted distributions of self-assembled structures. More generally, we show that the SA of water-floating robots is very sensitive to even small variations of the underlying physical parameters, thus requiring real-time tracking of its dynamics.

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Section: Motion Modelmentioning

confidence: 68%

A trajectory-based calibration method for stochastic motion models

Mario

Mermoud

Mastrangeli

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…However, this form of open-loop manipulation is not suitable to the microscale due to the increased precision requirements and the vastly different mechanics of manipulation. So, the closed-loop is a good solution to automate MEMS assembly processes essentially using vision-based control mechanisms (Yesin and Nelson, 2005), (Abbott et al, 2007). Among works relative to closed-loop strate-gies based on vision feedback, we can cite those of (Feddema and Simon, 1998), (Fatikow et al, (1999), (Ralis et al, 2000), (Yang et al, 2003), (Popa and Stephanou, 2004), (Yesin and Nelson, 2005).…”

Section: Motivationsmentioning

confidence: 99%

“…Indeed, high resolution images lead to precise measurements but very small field-of-view, like low resolution images enable large field-ofview but less precise information (Ralis et al, 2000), (Tao et al, 2005). To overcome this duality, two kinds of solutions had been implemented: a switch between the components of a multiple view system (Yang et al, 2003), (Popa and Stephanou, 2004), (Sun and Chin, 2004), (Abbott et al, 2007), (Probst et al, 2009), or integration of a dynamic zoom control in the visual control (Tao et al, 2005), (Tamadazte et al, 2008b). The other advantage of using a distributed vision system is the ability to have access to the depth of the scene using lateral views (Yang et al, 2005).…”

Section: Previous Research In Mems Assemblymentioning

confidence: 99%

CAD Model-based Tracking and 3D Visual-based Control for MEMS Microassembly

Tamadazte

Marchand

Dembélé

et al. 2010

The International Journal of Robotics Research

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This paper investigates sequential robotic microassembly for the construction of 3D micro-electro-mechanical systems (MEMS) structures using a 3D visual servoing approach. The previous solutions proposed in the literature for these kinds of problems are based on 2D visual control because of the lack of precise and robust 3D measures from the work scene. In this paper, the relevance of the real-time 3D visual tracking method and the 3D vision-based control law proposed is demonstrated. The 3D poses of the MEMS are supplied in real-time by a computer-aided design (CAD) model-based tracking algorithm. This latter is sufficiently accurate and robust to enable a precise regulation toward zero of the 3D error using the proposed pose-based visual servoing approach.Experiments on a microrobotic setup have been carried out to achieve assemblies of two or more 400 µm × 400 µm × 100 µm silicon micro-objects by their respective 97 µm × 97 µm × 100 µm notches with an assembly clearance from 1 µm to 5 µm. The different microassembly processes are performed with a mean error of 0.3 µm in position and 0.35×10 −2 rad in orientation.

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“…Untethered, controllable, mobile microrobots have been proposed for numerous applications, ranging from industrial tasks, in vitro tasks to in vivo applications, due to their small-scale dimensions and accessibility to tiny and complex environments [1][2][3][4][5][6][7][8][9]. Industrial tasks include micro-manipulation, transporting and sorting of micro-objects, which enables robotic micro-assembly [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Actuation Based Motion Control for Microrobots: An Overview

et al. 2015

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Untethered, controllable, mobile microrobots have been proposed for numerous applications, ranging from micro-manipulation, in vitro tasks (e.g., operation of microscale biological substances) to in vivo applications (e.g., targeted drug delivery; brachytherapy; hyperthermia, etc.), due to their small-scale dimensions and accessibility to tiny and complex environments. Researchers have used different magnetic actuation systems allowing custom-designed workspace and multiple degrees of freedom (DoF) to actuate microrobots with various motion control methods from open-loop pre-programmed control to closed-loop path-following control. This article provides an overview of the magnetic actuation systems and the magnetic actuation-based control methods for microrobots. An overall benchmark on the magnetic actuation system and control method is also discussed according to the applications of microrobots.

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Robotics in the Small, Part I: Microbotics

Cited by 311 publications

References 29 publications

A trajectory-based calibration method for stochastic motion models

A trajectory-based calibration method for stochastic motion models

CAD Model-based Tracking and 3D Visual-based Control for MEMS Microassembly

Magnetic Actuation Based Motion Control for Microrobots: An Overview

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