FBG based shape sensing of a silicone octopus tentacle model for soft robotics

Zhuang, Wei; Sun, Greg; Li, Hong; Lou, Xiaoping; Dong, Mingli; Zhu, Lianqing

doi:10.1016/j.ijleo.2018.03.087

Cited by 47 publications

(23 citation statements)

References 18 publications

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“…Just like a biological organism, constructing higher level behaviors such as decision-making, adaptation, and learning requires integrated multimodal sensors into soft-bodied systems to provide proprioception and exteroception sensory feedback. Thus, textile-based sensors (Farrow and Correll, 2015), ionic-liquid-based sensors (Russo et al, 2015), liquid-metal-based sensors (Morrow et al, 2016), nanocomposite-based sensors (Rocha et al, 2018;Tavakoli et al, 2017), and optical fiber sensors (Wang et al, 2016;Zhuang et al, 2018) have been attached/embodied in soft robots and prosthetic hands to sense and adapt to the environment.…”

Section: Closed-loop Control Based On Silicon Computationmentioning

confidence: 99%

Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration

Ren

Wei

et al. 2021

iScience

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Organisms in nature grow with senses, nervous, and actuation systems coordinated in ingenious ways to sustain metabolism and other essential life activities. The understanding of biological structures and functions guide the construction of soft robotics with unprecedented performances. However, despite the progress in soft robotics, there still remains a big gap between man-made soft robotics and natural lives in terms of autonomy, adaptability, self-repair, durability, energy efficiency, etc. Here, the actuation and sensing strategies in the natural biological world are summarized along with their man-made counterparts applied in soft robotics. The development trends of bioinspired soft robotics toward closed loop and embodiment are proposed. Challenges for obtaining autonomous soft robotics similar to natural organisms are outlined to provide a perspective in this field.

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Section: Closed-loop Control Based On Silicon Computationmentioning

confidence: 99%

Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration

Ren

Wei

et al. 2021

iScience

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“…Feedforward and feedback controllers were applied for the control of 3D/4D-printed SPAs (George Thuruthel et al 2018). Providing feedback information required for closed-loop control of SPAs demand availability of sensory feedback instruments that could be realized either using external measurement devices, such as camera (Marchese, Katzschmann, and Rus 2014) electromagnetic sensor (Wang et al 2014), optical sensor (Zhuang et al 2018;Darzi and Park 2017) or integrated flexible sensors (Khosravani and Reinicke 2020a) that do not compromise the conformability of the SPAs (Low et al 2017) ).…”

Section: Integration Of Sensorsmentioning

confidence: 99%

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Zolfagharian

Mahmud

Gharaie

et al. 2020

Virtual and Physical Prototyping

119

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This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can generate bending motions in response to either pressurized or vacuum (negative pressure) air stimulus after fabrication. They are characterized by geometrical and material factors that determine their motion trajectory, and the force they exert on manipulated soft objects in delicate applications such as food handling and non-invasive surgery. Here, we review various 3D printers and materials used for the fabrication of the pressurized air bendingtype SPAs. The reported approaches for modeling and control of these actuators are presented and compared. General discussions, as well as future directions and challenges of these actuators, are given.

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“…Many papers have focused on using optical methods that tend to revolve around novel combinations or topologies for Fiber Bragg Grating (FBG) sensors (see Wang et al, 2016 ; Zhuang et al, 2018 ; He et al, 2019 ; Sheng et al, 2019 ). However, other related methods focus on the basic idea of using optical fibers in general (see Yuan et al, 2011 ; Chen et al, 2019 ; Godaba et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement

2021

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This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.

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FBG based shape sensing of a silicone octopus tentacle model for soft robotics

Cited by 47 publications

References 18 publications

Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration

Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement

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