1,472 research outputs found
Strain Sensor-Embedded Soft Pneumatic Actuators for Extension and Bending Feedback
For soft robots to leave the lab and enter unstructured environments, proprioception is required to understand how interactions in the field affect the soft structure. In this work, we present sensor-embedded soft pneumatic actuators (sSPA) that can observe both extension and bending. The sensors are strain sensitive capacitors, which are bonded to the interior of fiber-reinforced extension actuators on opposing faces. This construction allows extension and bending to be measured by calculating the mean and difference in sensor responses, respectively. The sSPAs are bonded together to form a flat fascicle to increase the force output and prevent buckling under load, and are robust to component failure by incorporating redundancy. In this paper, we discuss the fabrication of the sensors and their subsequent integration into the actuators. We also report the work capacity and sensor. response of the sSPA fascicles under extension, bending, and the combination of both modes of deformation. The sensor- embedded soft pneumatic actuators presented here will advance the field of soft robotics by enabling closed-loop control of soft robots
Data-driven bending angle prediction of soft pneumatic actuators with embedded flex sensors
In this paper, resistive flex sensors have been embedded at the strain limiting layer of soft
pneumatic actuators, in order to provide sensory feedback that can be utilised in predicting their bending
angle during actuation. An experimental setup was prepared to test the soft actuators under controllable
operating conditions, record the resulting sensory feedback, and synchronise this with the actual bending
angles measured using a developed image processing program. Regression analysis and neural networks
are two data-driven modelling techniques that were implemented and compared in this study, to evaluate
their ability in predicting the bending angle response of the tested soft actuators at different input
pressures and testing orientations. This serves as a step towards controlling this class of soft bending
actuators, using data-driven empirical models that lifts the need for complex analytical modelling and
material characterisation. The aim is to ultimately create a more controllable version of this class of soft
pneumatic actuators with embedded sensing capabilities, to act as compliant soft gripper fingers that can
be used in applications requiring both a ‘soft touch’ as well as more controllable object manipulation
Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors - a data-driven approach
In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation
Soft Gloves: A Review on Recent Developments in Actuation, Sensing, Control and Applications
Interest in soft gloves, both robotic and haptic, has enormously grown over the past decade, due to their inherent compliance, which makes them particularly suitable for direct interaction with the human hand. Robotic soft gloves have been developed for hand rehabilitation, for ADLs assistance, or sometimes for both. Haptic soft gloves may be applied in virtual reality (VR) applications or to give sensory feedback in combination with prostheses or to control robots. This paper presents an updated review of the state of the art of soft gloves, with a particular focus on actuation, sensing, and control, combined with a detailed analysis of the devices according to their application field. The review is organized on two levels: a prospective review allows the highlighting of the main trends in soft gloves development and applications, and an analytical review performs an in-depth analysis of the technical solutions developed and implemented in the revised scientific research. Additional minor evaluations integrate the analysis, such as a synthetic investigation of the main results in the clinical studies and trials referred in literature which involve soft gloves
Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors: a data-driven approach
In this paper, a purely data-driven modelling approach is presented for predicting and controlling the free bending angle response of a typical soft pneumatic actuator (SPA), embedded with a resistive flex sensor. An experimental setup was constructed to test the SPA at different input pressure values and orientations, while recording the resulting feedback from the embedded flex sensor and on-board pressure sensor. A calibrated high speed camera captures image frames during the actuation, which are then analysed using an image processing program to calculate the actual bending angle and synchronise it with the recorded sensory feedback. Empirical models were derived based on the generated experimental data using two common data-driven modelling techniques; regression analysis and artificial neural networks. Both techniques were validated using a new dataset at untrained operating conditions to evaluate their prediction accuracy. Furthermore, the derived empirical model was used as part of a closed-loop PID controller to estimate and control the bending angle of the tested SPA based on the real-time sensory feedback generated. The tuned PID controller allowed the bending SPA to accurately follow stepped and sinusoidal reference signals, even in the presence of pressure leaks in the pneumatic supply. This work demonstrates how purely data-driven models can be effectively used in controlling the bending of SPAs under different operating conditions, avoiding the need for complex analytical modelling and material characterisation. Ultimately, the aim is to create more controllable soft grippers based on such SPAs with embedded sensing capabilities, to be used in applications requiring both a ‘soft touch’ as well as a more controllable object manipulation
A Review of Smart Materials in Tactile Actuators for Information Delivery
As the largest organ in the human body, the skin provides the important
sensory channel for humans to receive external stimulations based on touch. By
the information perceived through touch, people can feel and guess the
properties of objects, like weight, temperature, textures, and motion, etc. In
fact, those properties are nerve stimuli to our brain received by different
kinds of receptors in the skin. Mechanical, electrical, and thermal stimuli can
stimulate these receptors and cause different information to be conveyed
through the nerves. Technologies for actuators to provide mechanical,
electrical or thermal stimuli have been developed. These include static or
vibrational actuation, electrostatic stimulation, focused ultrasound, and more.
Smart materials, such as piezoelectric materials, carbon nanotubes, and shape
memory alloys, play important roles in providing actuation for tactile
sensation. This paper aims to review the background biological knowledge of
human tactile sensing, to give an understanding of how we sense and interact
with the world through the sense of touch, as well as the conventional and
state-of-the-art technologies of tactile actuators for tactile feedback
delivery
Control-based 4D printing: adaptive 4D-printed systems
Building on the recent progress of four-dimensional (4D) printing to produce dynamic structures, this study aimed to bring this technology to the next level by introducing control-based 4D printing to develop adaptive 4D-printed systems with highly versatile multi-disciplinary applications, including medicine, in the form of assisted soft robots, smart textiles as wearable electronics and other industries such as agriculture and microfluidics. This study introduced and analysed adaptive 4D-printed systems with an advanced manufacturing approach for developing stimuli-responsive constructs that organically adapted to environmental dynamic situations and uncertainties as nature does. The adaptive 4D-printed systems incorporated synergic integration of three-dimensional (3D)-printed sensors into 4D-printing and control units, which could be assembled and programmed to transform their shapes based on the assigned tasks and environmental stimuli. This paper demonstrates the adaptivity of these systems via a combination of proprioceptive sensory feedback, modeling and controllers, as well as the challenges and future opportunities they present
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