First-Order Dynamic Modeling and Control of Soft Robots

Thuruthel, Thomas George; Renda, Federico; Iida, Fumiya

doi:10.3389/frobt.2020.00095

Cited by 34 publications

(20 citation statements)

References 30 publications

(39 reference statements)

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“…Developing high-order dynamic models for soft robots is difficult and computationally expensive for controllers. A first-order dynamic modeling approach is proposed to reduce the computational space without affecting the controller's performance [107].…”

Section: Modelingmentioning

confidence: 99%

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

et al. 2022

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Nature and biological creatures are some of the main sources of inspiration for humans. Engineers have aspired to emulate these natural systems. As rigid systems become increasingly limited in their capabilities to perform complex tasks and adapt to their environment like living creatures, the need for soft systems has become more prominent due to the similar complex, compliant, and flexible characteristics they share with intelligent natural systems. This review provides an overview of the recent developments in the soft robotics field, with a focus on the underwater application frontier.

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Section: Modelingmentioning

confidence: 99%

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

et al. 2022

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“…The challenges in SCA control can be attributed mainly to the difficulties in modeling and sensing [2] its deformed shape. Current modeling methods are either simplistic with a constant curvature assumption that work in 2D plane or valid for SCAs with short lengths [3]. On the other hand, Cosserat models [4] require expert knowledge for their implementation and therefore have been less explored by the community.…”

Section: A Motivationmentioning

confidence: 99%

“…2020-67021-32799/project accession no.1024178 from the USDA National Institute of Food and Agriculture, by USDA-NSF NRI grant USDA 2019-67021-28989, NSF 1830343, and by joint NSF-USDA COALESCE grant, USDA 2021-67021-34418. 1 Computer Science, 2 Electrical and Computer Engineering, 3 Mechanical Science and Engineering, 4 Coordinated Science Laboratory, University of Illinois at Urbana Champaign, USA. (skk7, marri2, walt, uppalap2, gkrishna,girishc)@illinois.edu there aren't cost-effective sensors [5], [6] to get the spatial position feedback of SCAs.…”

Section: A Motivationmentioning

confidence: 99%

Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

Kamtikar,

Marri,

Walt

et al. 2022

Preprint

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For soft continuum arms, visual servoing is a popular control strategy that relies on visual feedback to close the control loop. However, robust visual servoing is challenging as it requires reliable feature extraction from the image, accurate control models and sensors to perceive the shape of the arm, both of which can be hard to implement in a soft robot. This letter circumvents these challenges by presenting a deep neural network-based method to perform smooth and robust 3D positioning tasks on a soft arm by visual servoing using a camera mounted at the distal end of the arm. A convolutional neural network is trained to predict the actuations required to achieve the desired pose in a structured environment. Integrated and modular approaches for estimating the actuations from the image are proposed and are experimentally compared. A proportional control law is implemented to reduce the error between the desired and current image as seen by the camera. The model together with the proportional feedback control makes the described approach robust to several variations such as new targets, lighting, loads, and diminution of the soft arm. Furthermore, the model lends itself to be transferred to a new environment with minimal effort.

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“…A first-order dynamic control of soft continuum robots, which also uses supervised learning to train a dynamic model representation using real experiments data, is presented in [20]. A simple control policy is trained reusing obtained real experiment data to perform the learning by skipping trajectory optimization steps.…”

Section: Introductionmentioning

confidence: 99%

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Wang,

Rojas

2022

Preprint

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Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry-which may cause potential damage to robots and be time consuming-and getting poorer performance when trained with simulation data only. This paper presents a modelbased learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets; control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics; control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-toreal gap in model-based learning for continuum robots.

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First-Order Dynamic Modeling and Control of Soft Robots

Cited by 34 publications

References 30 publications

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

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