Permanent Magnet-Based Localization for Growing Robots in Medical Applications

Watson, Connor; Morimoto, Tania K.

doi:10.1109/lra.2020.2972890

Cited by 31 publications

(11 citation statements)

References 57 publications

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“…Mounting inside the pressurized area does not require an active carriage device, as friction with the tail can passively keep devices at the tip during growth. Watson and Morimoto ( 2020 ) used this method to keep a ring magnet at the tip of a millimeter-scale everting vine robot for tip-localization.…”

Section: Designmentioning

confidence: 99%

Design, Modeling, Control, and Application of Everting Vine Robots

et al. 2020

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In nature, tip-localized growth allows navigation in tightly confined environments and creation of structures. Recently, this form of movement has been artificially realized through pressure-driven eversion of flexible, thin-walled tubes. Here we review recent work on robots that “grow” via pressure-driven eversion, referred to as “everting vine robots,” due to a movement pattern that is similar to that of natural vines. We break this work into four categories. First, we examine the design of everting vine robots, highlighting tradeoffs in material selection, actuation methods, and placement of sensors and tools. These tradeoffs have led to application-specific implementations. Second, we describe the state of and need for modeling everting vine robots. Quasi-static models of growth and retraction and kinematic and force-balance models of steering and environment interaction have been developed that use simplifying assumptions and limit the involved degrees of freedom. Third, we report on everting vine robot control and planning techniques that have been developed to move the robot tip to a target, using a variety of modalities to provide reference inputs to the robot. Fourth, we highlight the benefits and challenges of using this paradigm of movement for various applications. Everting vine robot applications to date include deploying and reconfiguring structures, navigating confined spaces, and applying forces on the environment. We conclude by identifying gaps in the state of the art and discussing opportunities for future research to advance everting vine robots and their usefulness in the field.

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Section: Designmentioning

confidence: 99%

Design, Modeling, Control, and Application of Everting Vine Robots

et al. 2020

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“…Watson and Morimoto proposed to localize the tip of soft continuum robots that have potential to be usable as medical devices in which the medical field needs accurate control to guarantee safety. They used a LSTM to localize the magnet at the tip of the robot compared to existing analytic and hybrid methods [ 124 ]. In [ 125 ], a hybrid model for controlling a modular collaborative Variable-Stiffness-Link (VSL) robots has been proposed.…”

Section: Methodsmentioning

confidence: 99%

Review of machine learning methods in soft robotics

Kim

et al. 2021

PLoS ONE

150

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Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

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“…For example, robots can support the independence of people with disabilities by enabling transitions to home-based care. Robots can also help clinicians and caregivers with care tasks including physical, cognitive, and manipulation tasks [20, 88,95,160,198], as well as healthcare worker education (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

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Permanent Magnet-Based Localization for Growing Robots in Medical Applications

Cited by 31 publications

References 57 publications

Design, Modeling, Control, and Application of Everting Vine Robots

Design, Modeling, Control, and Application of Everting Vine Robots

Review of machine learning methods in soft robotics

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

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