Composite force sensing foot utilizing volumetric displacement of a hyperelastic polymer

Chuah, Meng Yee; Estrada, Matthew A.; Kim, Sangbae

doi:10.1109/iros.2012.6386239

Cited by 7 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All applications mentioned in the previous section rely on force sensors; on the market and in scientific literature there are several transducers used to convert force into an electric quantity [20,21]; each of these has its own advantages and disadvantages. In the following, we will use the terms force and pressure as synonyms, considering that force is pressure over an area, assuming that the area is known.…”

Section: Force Sensing Overviewmentioning

confidence: 99%

“…Here the subdivision is made using the physical quantity exploited to quantify the force. In this context we find sensors involving a variation of an electrical property (resistance, capacitance, or more generally impedance), sensors generating a charge displacement (piezoelectric), and others that use different physical quantities (light, magnetic field) to measure the displacement variation of a known material [20][21][22]; also out-ofthe-box barometric MEMS (Micro Electromechanical Systems) can be used to measure interface pressure [21,23].…”

Section: Force Sensing Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

2016

View full text Add to dashboard Cite

Wearable technologies are gaining momentum and widespread diffusion. Thanks to devices such as activity trackers, in form of bracelets, watches, or anklets, the end-users are becoming more and more aware of their daily activity routine, posture, and training and can modify their motor-behavior. Activity trackers are prevalently based on inertial sensors such as accelerometers and gyroscopes. Loads we bear with us and the interface pressure they put on our body also affect posture. A contact interface pressure sensing wearable would be beneficial to complement inertial activity trackers. What is precluding force sensing resistors (FSR) to be the next best seller wearable? In this paper, we provide elements to answer this question. We build an FSR based on resistive material (Velostat) and printed conductive ink electrodes on polyethylene terephthalate (PET) substrate; we test its response to pressure in the range 0–2.7 kPa. We present a state-of-the-art review, filtered by the need to identify technologies adequate for wearables. We conclude that the repeatability is the major issue yet unsolved.

show abstract

Section: Force Sensing Overviewmentioning

confidence: 99%

Section: Force Sensing Overviewmentioning

confidence: 99%

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

2016

View full text Add to dashboard Cite

show abstract

“…The rigid lining provided structural integrity and allowed for the PCB to be anchored onto the foot. This approach was used in the previous prototype footpad made with magnets and Hall-effect sensors, as described in [16]. The results in this paper are solely from the prototype footpad utilizing barometric pressure sensors.…”

Section: B Footpad Design and Fabricationmentioning

confidence: 99%

“…A typical commercial sensor will suffer from inertial noise under high acceleration and has been experimentally verified in a commercial F/T sensor under shaking. 2 Hence it is not viable to use F/T sensors for high speed locomotion and an alternative force sensing method is needed [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enabling Force Sensing During Ground Locomotion: A Bio-Inspired, Multi-Axis, Composite Force Sensor Using Discrete Pressure Mapping

Chuah

Kim

2014

IEEE Sensors J.

Self Cite

View full text Add to dashboard Cite

Abstract-The paper presents a new force sensor design approach that maps the local sampling of pressure inside a composite polymeric footpad to forces in three axes, designed for running robots. Conventional multi-axis force sensors made of heavy metallic materials tend to be too bulky and heavy to be fitted in the feet of legged robots, and vulnerable to inertial noise upon high acceleration. To satisfy the requirements for high speed running, which include mitigating high impact forces, protecting the sensors from ground collision and enhancing traction, these stiff sensors should be paired with additional layers of durable, soft materials; but this also degrades the integrity of the foot structure. The proposed foot sensor is manufactured as a monolithic, composite structure composed of an array of barometric pressure sensors completely embedded in a protective polyurethane rubber layer. This composite architecture allow the layers to provide compliance and traction for foot collision while the deformation and the sampled pressure distribution of the structure can be mapped into three axis force measurement. Normal and shear forces can be measured upon contact with the ground, which causes the footpad to deform and change the readings of the individual pressure sensors in the array. A onetime training process using an artificial neural network is all that is necessary to relate the normal and shear forces with the multi-axis foot sensor output. The results show that the sensor can predict normal forces in the Z-axis up to 300N with a root mean squared error of 0.66% and up to 80N in the X-and Yaxis. The experiment results demonstrates a proof-of-concept for a lightweight, low cost, yet robust footpad sensor suitable for use in legged robots undergoing ground locomotion.

show abstract

Improved normal and shear tactile force sensor performance via Least Squares Artificial Neural Network (LSANN)

Chuah

Kim

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents a new approach to the characterization of tactile array sensors that aims to reduce the computational time needed for convergence to obtain a useful estimator for normal and shear forces. This is achieved by breaking up the sensor characterization into two parts: a linear regression portion using multivariate least squares regression, and a nonlinear regression portion using a neural network as a multi-input, multi-output function approximator. This procedure has been termed Least Squares Artificial Neural Network (LSANN). By applying LSANN on the 2nd generation MIT Cheetah footpad, the convergence speed for the estimator of the normal and shear forces is improved by 59.2% compared to using only the neural network alone. The normalized root mean squared error between the two methods are nearly identical at 1.17% in the normal direction, and 8.30% and 10.14% in the shear directions. This approach could have broader implications in greatly reducing the amount of time needed to train a contact force estimator for a large number of tactile sensor arrays (i.e. in robotic hands and skin).

show abstract

Composite force sensing foot utilizing volumetric displacement of a hyperelastic polymer

Cited by 7 publications

References 25 publications

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing

Enabling Force Sensing During Ground Locomotion: A Bio-Inspired, Multi-Axis, Composite Force Sensor Using Discrete Pressure Mapping

Improved normal and shear tactile force sensor performance via Least Squares Artificial Neural Network (LSANN)

Contact Info

Product

Resources

About