2017 IEEE International Conference on Robotics and Automation (ICRA) 2017
DOI: 10.1109/icra.2017.7989577
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A method for sensorizing soft actuators and its application to the RBO hand 2

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Cited by 60 publications
(45 citation statements)
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“…4i), building on designs introduced by Majidi and Park et al 99 . They are also useful in sensing strain in pneumatic systems, since they refrain from using stiff components 23,107,108 , and can even be used in transparent devices 109 . As with the deterministic approach, some of these devices take explicit steps towards untethered functionality by incorporating NFC features to output sensor data wirelessly 83 .…”
Section: Nature Electronicsmentioning
confidence: 99%
See 1 more Smart Citation
“…4i), building on designs introduced by Majidi and Park et al 99 . They are also useful in sensing strain in pneumatic systems, since they refrain from using stiff components 23,107,108 , and can even be used in transparent devices 109 . As with the deterministic approach, some of these devices take explicit steps towards untethered functionality by incorporating NFC features to output sensor data wirelessly 83 .…”
Section: Nature Electronicsmentioning
confidence: 99%
“…Pneumatic and hydraulic actuators have long been popular among soft actuation methods thanks to their relatively large actuation forces, fast response times, high work densities and large strains (Table 1). Much recent work has focused on incorporating established pneumatic actuators into robotic locomotion 21,22 , grasping 23 and wearable haptic 24 or assistive devices 10 .…”
Section: Soft Actuationmentioning
confidence: 99%
“…These grippers tend to be fluid-driven with interior chambers that upon addition / removal of fluid, close up and grasp around an object. Efforts towards sensorizing soft material grippers have utilized liquid metal-based deformation sensors to track motion of the gripper fingers [9,22,23], conductive elastomerbased strain and pressure sensors for haptic-based mapping of object contours [24], commercially-available bend sensors to classify objects [6], and either conductive elastomer or liquid metal-based sensors to control the degree of grasp of the gripper [14,25]. While these soft material grippers have demonstrated success in passively conforming to grasp objects, a fluid-driven actuation system can be unwieldy and difficult to implement.…”
Section: Introductionmentioning
confidence: 99%
“…Liquid metal microchannel sensors have been embedded in linearly contracting pneumatic artificial muscles as radial and axial sensors [3]- [6]. Bending actuators, including those used for soft pneumatic grippers, have been fabricated with liquid metal microchannels to create resistive sensors for determining bend angle, degree of grasp, or contact with an object [7]- [11]. By adding a redundancy of strain sensors around a pneumatic actuator (more sensors than degrees of freedom) and using machine learning techniques, researchers have shown that bending motions in 3D space can be reconstructed [11].…”
Section: Introductionmentioning
confidence: 99%
“…Bending actuators, including those used for soft pneumatic grippers, have been fabricated with liquid metal microchannels to create resistive sensors for determining bend angle, degree of grasp, or contact with an object [7]- [11]. By adding a redundancy of strain sensors around a pneumatic actuator (more sensors than degrees of freedom) and using machine learning techniques, researchers have shown that bending motions in 3D space can be reconstructed [11]. Other emerging approaches for sensing motion of pneumatic actuators are induction sensing with coiled wires to detect contraction of bellows actuators [12], [13], resistive sensing using silversilicone conductive composites to measure bending angle [14], magnetic sensing between joints using a magnetic silicone composite [15], optical waveguides for sensing strain along a pneumatic actuator [16], [17], and siliconebased capacitive sensors embedded within a fabric sleeve to measure the bend angle of a bellows-actuated robotic arm [18].…”
Section: Introductionmentioning
confidence: 99%