Non-linear System Identification and State Estimation in a Pneumatic Based Soft Continuum Robot

Loo, Junn Yong; Kong, Kah Chun; Tan, Chee Pin; Nurzaman, Surya G.

doi:10.1109/ccta.2019.8920693

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…In order to reduce the stiffness of the continuum robot while maintaining a larger workspace, the fluid actuation mechanism [55][56][57] is proposed to replace the tendon actuation. Although its soft body gives the robot the advantages of safe contact and a large bending angle, it is difficult to achieve accurate positioning due to its highly nonlinear characteristics and the increasing complexity of the control system [58]. In addition, fluid-driven robots are larger than tendon-driven robots due to their limited actuation mode in shape.…”

Section: Classification Of the Design And Actuation Principlementioning

confidence: 99%

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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In this paper, we describe the advances in the design, actuation, modeling, and control field of continuum robots. After decades of pioneering research, many innovative structural design and actuation methods have arisen. Untethered magnetic robots are a good example; its external actuation characteristic allows for miniaturization, and they have gotten a lot of interest from academics. Furthermore, continuum robots with proprioceptive abilities are also studied. In modeling, modeling approaches based on continuum mechanics and geometric shaping hypothesis have made significant progress after years of research. Geometric exact continuum mechanics yields apparent computing efficiency via discrete modeling when combined with numerical analytic methods such that many effective model-based control methods have been realized. In the control, closed-loop and hybrid control methods offer great accuracy and resilience of motion control when combined with sensor feedback information. On the other hand, the advancement of machine learning has made modeling and control of continuum robots easier. The data-driven modeling technique simplifies modeling and improves anti-interference and generalization abilities. This paper discusses the current development and challenges of continuum robots in the above fields and provides prospects for the future.

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Section: Classification Of the Design And Actuation Principlementioning

confidence: 99%

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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“…Hence, cascade controllers with the incorporation of machine learning algorithms to tune the PID gains could be adopted for improving the accuracy of the end-point position tracking of SPA (Zhao, Zhong, and Fan 2015;Fan et al 2015) (Zolfagharian, Valipour, and Ghasemi 2016). A nonlinear autoregressive-exogenous (NARX) observer, using wavelet and sigmoid networks was also developed in the form of an Extended Kalman Filter to predict the behavior of the SPA operating in an unstructured environment with minimum error (Loo et al 2019). The schematic workflow for the data-driven modeling and control of 3D/4D-printed SPAs is illustrated in Fig.…”

Section: Data-driven Machine Learning Modeling and Controlmentioning

confidence: 99%

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

Zolfagharian

Mahmud

Gharaie

et al. 2020

Virtual and Physical Prototyping

121

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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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“…The majority of approaches that apply statistical methods to estimate the robot’s end-effector pose or shape are based on simplified kinematic models that assume bending in constant curvatures. Examples include shape estimation of catheters (Brij Koolwal et al, 2010; Borgstadt et al, 2015), tendon-driven continuum robots (Chen et al, 2019; Ataka et al, 2016), and soft robots (Loo et al, 2019). In another approach, Lobaton et al (2013) incorporate a more general shape representation, consisting of a combination of spatial basis functions, with a stochastic state estimation approach.…”

Section: Introductionmentioning

confidence: 99%

Continuum robot state estimation using Gaussian process regression on SE(3)

Lilge

Barfoot

Burgner-Kahrs

2022

The International Journal of Robotics Research

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Continuum robots have the potential to enable new applications in medicine, inspection, and countless other areas due to their unique shape, compliance, and size. Excellent progress has been made in the mechanical design and dynamic modeling of continuum robots, to the point that there are some canonical designs, although new concepts continue to be explored. In this paper, we turn to the problem of state estimation for continuum robots that can been modeled with the common Cosserat rod model. Sensing for continuum robots might comprise external camera observations, embedded tracking coils, or strain gauges. We repurpose a Gaussian process (GP) regression approach to state estimation, initially developed for continuous-time trajectory estimation in SE(3). In our case, the continuous variable is not time but arclength and we show how to estimate the continuous shape (and strain) of the robot (along with associated uncertainties) given discrete, noisy measurements of both pose and strain along the length. We demonstrate our approach quantitatively through simulations as well as through experiments. Our evaluations show that accurate and continuous estimates of a continuum robot’s shape can be achieved, resulting in average end-effector errors between the estimated and ground truth shape as low as 3.5 mm and 0.016° in simulation or 3.3 mm and 0.035° for unloaded configurations and 6.2 mm and 0.041° for loaded ones during experiments, when using discrete pose measurements.

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Non-linear System Identification and State Estimation in a Pneumatic Based Soft Continuum Robot

Cited by 17 publications

References 34 publications

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

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

Continuum robot state estimation using Gaussian process regression on SE(3)

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