Kinematics of Soft Robots by Geometric Computing

Fang, Guoxin; Matte, Christopher-Denny; Scharff, Rob B. N.; Kwok, Tsz-Ho; Wang, Charlie C. L.

doi:10.1109/tro.2020.2985583

Cited by 43 publications

(28 citation statements)

References 46 publications

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“…In this work, we present an optimization-based simulator that can efficiently predict the complicated deformation on soft robots and effectively provide realistic collision response incorporating hyperelastic material property. Our method extends the pipeline of geometry computing previously presented in [16], [23]. The elastic energy in soft body is minimized to compute its deformed shape, and the actuation is formulated as constraints in terms of length, area or volume variation applied to some elements.…”

Section: Our Methodsmentioning

confidence: 99%

“…1) Complexity in deformation: The involvement of highly nonlinear deformation will make FEA-based simulation difficult to converge in numerical iteration [16]. For example, the largely inflated chambers for pneumaticdriven soft robot can lead to highly-distorted elements for FEA, which brings in numerical instability.…”

Section: A Challenges In Collision-aware Simulation For Soft Robotsmentioning

confidence: 99%

“…Here a target volume expansion ratio α is given as the input actuation parameter. In the geometry-based simulation pipeline [16], shapes of the body elements were computed by minimizing the elastic energy E ela . This energy is evaluated by the shape difference between the element's deformed shape V d and its target shape V t (details will be presented in Sec.…”

Section: A Geometry-based Optimization For Modelling Soft Robotsmentioning

confidence: 99%

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Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Fang¹,

Tian²,

Weightman³

et al. 2022

Preprint

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Soft robots are able to safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance in different tasks. However, developing an efficient and reliable simulator which can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometrybased objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Different from the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots having large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach.

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Section: Our Methodsmentioning

confidence: 99%

Section: A Challenges In Collision-aware Simulation For Soft Robotsmentioning

confidence: 99%

Section: A Geometry-based Optimization For Modelling Soft Robotsmentioning

confidence: 99%

See 1 more Smart Citation

Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Fang¹,

Tian²,

Weightman³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Of course, this is not without its challenges. The lack of stiffness, for example, makes the kinematics and dynamics of the soft robot significantly more complex than that of its rigid counterpart, especially during the interaction with the environment [24]. Similarly, precise control of a soft robot's movement and its positional tracking are also rendered more difficult.…”

Section: Considerations For the Design Of Soft Surgical Robots Integr...mentioning

confidence: 99%

Fusing Dexterity and Perception for Soft Robot-Assisted Minimally Invasive Surgery: What We Learnt from STIFF-FLOP

et al. 2021

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In recent years we have seen tremendous progress in the development of robotic solutions for minimally invasive surgery (MIS). Indeed, a number of robot-assisted MIS systems have been developed to product level and are now well-established clinical tools; Intuitive Surgical’s very successful da Vinci Surgical System a prime example. The majority of these surgical systems are based on the traditional rigid-component robot design that was instrumental in the third industrial revolution—especially within the manufacturing sector. However, the use of this approach for surgical procedures on or around soft tissue has come under increasing criticism. The dangers of operating with a robot made from rigid components both near and within a patient are considerable. The EU project STIFF-FLOP, arguably the first large-scale research programme on soft robots for MIS, signalled the start of a concerted effort among researchers to investigate this area more comprehensively. While soft robots have many advantages over their rigid-component counterparts, among them high compliance and increased dexterity, they also bring their own specific challenges when interacting with the environment, such as the need to integrate sensors (which also need to be soft) that can determine the robot’s position and orientation (pose). In this study, the challenges of sensor integration are explored, while keeping the surgeon’s perspective at the forefront of ourdiscussion. The paper critically explores a range of methods, predominantly those developed during the EU project STIFF-FLOP, that facilitate the embedding of soft sensors into articulate soft robot structures using flexible, optics-based lightguides. We examine different optics-based approaches to pose perception in a minimally invasive surgery settings, and methods of integration are also discussed.

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“…This makes it inherently difficult for designers to predict how even small changes to design parameters such as tendon placement impacts the overall capabilities of the hand design [7], [10]. Consequently, design candidates must often be fabricated and tested in the real world or require specialized simulation for evaluation [14], [16], [28], [15]. Unfortunately, state-of-the art soft body simulators [36], [11] are not able to provide effective, efficient, and robust evaluation of design candidates for design optimization techniques [10].…”

Section: A Design and Modelingmentioning

confidence: 99%

Towards Very Low-Cost Iterative Prototyping for Fully Printable Dexterous Soft Robotic Hands

Bauer¹,

Bauer²,

Lakshmipathy³

et al. 2021

Preprint

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The design and fabrication of soft robot hands is still a time-consuming and difficult process. Advances in rapid prototyping have accelerated the fabrication process significantly while introducing new complexities into the design process. In this work, we present an approach that utilizes novel low-cost fabrication techniques in conjunction with design tools helping soft hand designers to systematically take advantage of multi-material 3D printing to create dexterous soft robotic hands. While very low cost and lightweight, we show that generated designs are highly durable, surprisingly strong, and capable of dexterous grasping.

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Kinematics of Soft Robots by Geometric Computing

Cited by 43 publications

References 46 publications

Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Fusing Dexterity and Perception for Soft Robot-Assisted Minimally Invasive Surgery: What We Learnt from STIFF-FLOP

Towards Very Low-Cost Iterative Prototyping for Fully Printable Dexterous Soft Robotic Hands

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