Towards flexible magnetoelectronics for robotic applications

Heidari, Hadi; Liu, Fengyuan; Dahiya, Ravinder

doi:10.1109/acirs.2017.7986111

Cited by 4 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensors provided contact less switching action and memory devices such as RAM chips and so on for the robots. The data from the magnetosensors such as Hall effect, anisotropic magneto resistance (AMR), giant magneto resistance (GMR) and tunneling magneto resistance (TMR) sensors provide valuable instantaneous information which is processed by feedback control systems and analog data circuitry to estimate the dynamic motion and vision of the robot (Heidari et al , 2017). Real-time 3D stress field forces acting on a flexible conductive material were analyzed using electrical resistance tomography using inversion method.…”

Section: Review Of Shape Sensing Methods and Fiber Optics Based Shape Sensingmentioning

confidence: 99%

“…In the industrial domain, most review papers focused on the following: principles of flexible pressure sensors (Ashruf, 2002), piezoresistive-based strain sensors (Alamusi et al , 2011) and carbon nanotube (CNT)-based strain sensors for twist, bend and stretch direction detection (Obitayo and Liu, 2012; Stassi et al , 2014); resistive flexible sensors and bendable ultrathin flexible sensors (Saggio et al , 2015; Heidari et al , 2017); technological advancements in flexible pressure sensors arranged in an array format (Xu et al , 2018; Li et al , 2018); diverse applications and theories behind piezoresistive sensors (Fiorillo et al , 2018); and modern fabrication techniques and new materials used for CNT-based energy harvesting (Han et al , 2019; Parameswaran and Gupta, 2019; Jeong et al , 2020). …”

Section: Introductionmentioning

confidence: 99%

“…resistive flexible sensors and bendable ultrathin flexible sensors (Saggio et al , 2015; Heidari et al , 2017);…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recent trends and role of large area flexible electronics in shape sensing application – a review

Shaik

Rufus

2021

View full text Add to dashboard Cite

Purpose This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused. Design/methodology/approach Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose. Findings LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction. Research limitations/implications The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing. Practical implications Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely. Social implications LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace. Originality/value This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

show abstract

Section: Review Of Shape Sensing Methods and Fiber Optics Based Shape Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recent trends and role of large area flexible electronics in shape sensing application – a review

Shaik

Rufus

2021

View full text Add to dashboard Cite

show abstract

“…Until now, there are four major generations of magnetoresistive sensors: Hall effect sensors, AMR sensors, giant magnetoresistance (GMR) sensors, and TMR sensors [10].…”

Section: B Magnetic Sensing Technologiesmentioning

confidence: 99%

PKBPNN-Based Tracking Range Extending Approach for TMR Magnetic Tracking System

Chen

Dai

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The magnetic tracking system, which is based on a permanent magnet and a magnetometer array, has numerous potential applications in the biomedical and industrial area. However, its tracking accuracy drops off sharply with the increase of tracking distance because of both the interference of environmental magnetic field and sensor measurement noise. To extend the magnetic tracking range, two novel methods were proposed in this study. First, the state-of-the-art tri-axial tunnel magnetoresistance (TMR) sensors, which possess the most significant field sensitivity and signal-to-noise ratio (SNR) over other types of magnetoresistive sensors, were adopted to construct the sensor array. Second, a fusion approach was proposed for tracking range extending. The particle swarm optimization-Levenberg Marquardt (PSO-LM) method based on the magnetic dipole model was applied in the near field (i.e., near-source zone), and the prior knowledge based back propagation neural network (PKBPNN) was adopted to the far field (i.e., far-source zone). In the transition zone, these two algorithms were fused by using an adaptive sigmoid function. The trained artificial neural network (ANN) model embodied the physical model errors, the sensor installation errors, and the inherent characteristics of adopted magnetometers. Therefore, it has greater tracking performance than singly using the PSO-LM in the far-source zone. The experimental results show that the tracking errors decrease from (18.24 ± 9.37mm, 12.45 ± 3.37 • ) to (8.95 ± 1.74mm, 7.97 ± 2.08 • ) in the tracking range between 216 and 296 mm. Besides, the tracking distance is extended to 396 mm, with the position error of less than 25 mm. It can be concluded that this approach has significantly extended the tracking range of the magnetic tracking system.INDEX TERMS Back propagation neural network (BPNN), magnetic tracking, prior knowledge, TMR, tracking range.

show abstract

“…The brain micromotions inside the skull dictate two key factors that should be considered in the probe design, i.e., (1) flexibility and ( 2) miniaturization. This paper investigates the mechanical behaviour of neural microprobes based on flexible materials [9][10][11] as compared against the behaviour of microprobes made of non-flexible material. We use the finite element method in COMSOL Multiphysics to model and optimize the microprobe considering specific design requirements for application to the target brain area and the required mechanical stiffness for successful insertion of the probe to overcome its mechanical failure due to buckling and fracture.…”

Section: Introductionmentioning

confidence: 99%

Neural Microprobe Device Modelling for Implant Micromotions Failure Mitigation

Nabaei

Panuccio

Heidari

2020

2020 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

View full text Add to dashboard Cite

Brain micromotion is a major contributor to the failure of implantable neural interfaces. Brain micromotions and tissue damage can be effectively reduced in two ways: (i) miniaturization of the implantable device footprint and (ii) choosing flexible materials for the device substrate. To meet these requirements, in this work we perform two sets of modelling using finite element method in COMSOL Multiphysics. First, we model the performance of different materials ranging from stiff (e.g. Silicon) to very soft (e.g. PDMS) with different sizes to find the optimal dimension and material for the microprobe. For the device size optimization, the main degree of freedom is thickness, while the minimum shank width and length depend on the recording sites and the target recording point, respectively. Modelling devices with different thicknesses (50 -200 μm) and fixed shank width (100 µm) based on different substrates, we show that the Polyimidebased microprobe exhibits a safety factor of 5 to 15 and maximum von mises stress of 248-770 MPa. Further, simulations indicate that the Polyimide-based microprobe of 50 μm thickness, exhibiting safety factor of 5 and stress of 248 MPa, provides the optimal solution in terms of size and material. Second, to analyse the device shape factor, we model different layouts based on the obtained optimal design and find that the optimal layout features von mises stress of 134.123 MPa, which is versatile and suitable to be used as microprobe especially for the brain micromotion effects mitigation purpose.

show abstract

Towards flexible magnetoelectronics for robotic applications

Cited by 4 publications

References 26 publications

Recent trends and role of large area flexible electronics in shape sensing application – a review

Recent trends and role of large area flexible electronics in shape sensing application – a review

PKBPNN-Based Tracking Range Extending Approach for TMR Magnetic Tracking System

Neural Microprobe Device Modelling for Implant Micromotions Failure Mitigation

Contact Info

Product

Resources

About