Twisted and coiled sensor for shape estimation of soft robots

Abbas, Ali; Zhao, Jianguo

doi:10.1109/iros.2017.8202197

Cited by 27 publications

(8 citation statements)

References 34 publications

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“…The length information of these three chambers was utilized in dedicated equations to estimate the arc parameters of the manipulator. Abbas et al (Abbas and Zhao, 2017) also presented another method that used a twisted and coiled sensor to estimate the shape of a soft robot. This sensor was made as twisted and coiled shape configuration using silver-coated nylon sewing threads.…”

Section: Passive Stretchable Sensor-based Shape Reconstruction Processesmentioning

confidence: 99%

Shape Reconstruction Processes for Interventional Application Devices: State of the Art, Progress, and Future Directions

et al. 2021

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Soft and continuum robots are transforming medical interventions thanks to their flexibility, miniaturization, and multidirectional movement abilities. Although flexibility enables reaching targets in unstructured and dynamic environments, it also creates challenges for control, especially due to interactions with the anatomy. Thus, in recent years lots of efforts have been devoted for the development of shape reconstruction methods, with the advancement of different kinematic models, sensors, and imaging techniques. These methods can increase the performance of the control action as well as provide the tip position of robotic manipulators relative to the anatomy. Each method, however, has its advantages and disadvantages and can be worthwhile in different situations. For example, electromagnetic (EM) and Fiber Bragg Grating (FBG) sensor-based shape reconstruction methods can be used in small-scale robots due to their advantages thanks to miniaturization, fast response, and high sensitivity. Yet, the problem of electromagnetic interference in the case of EM sensors, and poor response to high strains in the case of FBG sensors need to be considered. To help the reader make a suitable choice, this paper presents a review of recent progress on shape reconstruction methods, based on a systematic literature search, excluding pure kinematic models. Methods are classified into two categories. First, sensor-based techniques are presented that discuss the use of various sensors such as FBG, EM, and passive stretchable sensors for reconstructing the shape of the robots. Second, imaging-based methods are discussed that utilize images from different imaging systems such as fluoroscopy, endoscopy cameras, and ultrasound for the shape reconstruction process. The applicability, benefits, and limitations of each method are discussed. Finally, the paper draws some future promising directions for the enhancement of the shape reconstruction methods by discussing open questions and alternative methods.

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Section: Passive Stretchable Sensor-based Shape Reconstruction Processesmentioning

confidence: 99%

Shape Reconstruction Processes for Interventional Application Devices: State of the Art, Progress, and Future Directions

et al. 2021

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“…Besides using SCPAM as the actuator of robots, some research results have already shown that it is feasible to use SCPAM as an internal strain sensor. The one-ply SCPAM string was embedded into the robot to estimate the externally applied load and length change of the robot based on the established model and measured resistance values during stretching or twisting [ 42 ]. Additionally, Zhao et al integrated stretchable optomechanical thin film sensors into SCPAM strings to obtain SCPAM strings with sensing capability, and the SCPAM strings appeared as different colors for different strains under the impact of the current [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

A Proprioceptive Soft Robot Module Based on Supercoiled Polymer Artificial Muscle Strings

et al. 2022

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In this paper, a multi-functional soft robot module that can be used to constitute a variety of soft robots is proposed. The body of the soft robot module made of rubber is in the shape of a long strip, with cylindrical chambers at both the top end and bottom end of the module for the function of actuators and sensors. The soft robot module is driven by supercoiled polymer artificial muscle (SCPAM) strings, which are made from conductive nylon sewing threads. Artificial muscle strings are embedded in the chambers of the module to control its deformation. In addition, SCPAM strings are also used for the robot module’s sensing based on the linear relationship between the string’s length and their resistance. The bending deformation of the robot is measured by the continuous change of the sensor’s resistance during the deformation of the module. Prototypes of an inchworm-like crawling robot and a soft robotic gripper are made, whose crawling ability and grasping ability are tested, respectively. We envision that the proposed proprioceptive soft robot module could potentially be used in other robotic applications, such as continuum robotic arm or underwater robot.

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“…Resistance-based sensing is often used with conductive material or fabrics that are assembled in a way such that the resistance of a circuit varies as the bend angle of the robot changes. Examples use methods ranging from commercial flex sensors (Ozel et al, 2016 ), to conductive thread (Cianchetti et al, 2012 ; Zhao and Abbas, 2016 ; Abbas and Zhao, 2017 ), or yarn (Wurdemann et al, 2015 ), to conductive silicone that is cut using principles from kirigami (Truby et al, 2020 ). There are multiple examples of this approach (see Gibbs and Asada, 2005 ; She et al, 2015 ; Elgeneidy et al, 2016 , 2018 ; Yuen et al, 2017 ; Zhou 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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Twisted and coiled sensor for shape estimation of soft robots

Cited by 27 publications

References 34 publications

Shape Reconstruction Processes for Interventional Application Devices: State of the Art, Progress, and Future Directions

Shape Reconstruction Processes for Interventional Application Devices: State of the Art, Progress, and Future Directions

A Proprioceptive Soft Robot Module Based on Supercoiled Polymer Artificial Muscle Strings

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

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