Kinematic Modeling and Control of Variable Curvature Soft Continuum Robots

Huang, Xinjia; Zou, Jiang; Gu, Guoying

doi:10.1109/tmech.2021.3055339

Cited by 74 publications

(30 citation statements)

References 43 publications

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“…从整体来看, 目前关于智能材料和智能结构的研究相对深入, 而智能形态的研究相对不足. 从局部来看, 例如从驱动的角度, 电驱动的物理智能系统更多地聚焦于智能材料的性能研究(如材料的非线性问题 [119] ), 对于智能形态方面的研究较少涉及; 而气动的物理智能系统由于较少应用智能材料, 在智能形态方面已有较深研究(如形态控制 [120] ).…”

Section: 物理智能系统集成目前越来越多的研究将智能材料与智能结构结合在一起融合力学和材料科学的前沿unclassified

Bio-inspired physical intelligence for soft robotics

Wang¹,

Xie²,

Yuan³

et al. 2022

Chin. Sci. Bull.

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and structure, self-learning, self-healing, and self-decision-making into physical bodies of the biological or robotic agents. Physical intelligence can also be generated during the interaction between the agent's body and the environment over time. The previous focus of intelligent robots is primarily focused on the computational intelligence. As a new paradigm, physical intelligence is expected to boost intelligent robots in real-world applications.Soft robots commonly use soft stimuli-responsive materials and intelligent structures and maintain high stretchability and considerable deformation, therefore, have intrinsic environmental conformable physical property. Thus, soft robots are essential platforms for testing hypothesis of physical intelligence in nature. This review mainly focused on three key physical intelligence elements encoded in the natural organisms' bodies: material, structure, and morphology. Through bio-inspired design, smart soft materials, smart soft structures, and adaptable morphologies can be integrated into the robot's body, thus introducing bio-inspired physical intelligence into the robots. By integrating bio-inspired physical intelligence, soft robots could reduce the cost of control, improve the response speed of the systems, enhance the robustness of robots in extreme environments, and embed intelligence into the micro-and small-scale robots. The research on bio-inspired physical intelligence A c c e p t e d https://engine.scichina.com/doi/10.1360/TB-2021-1217 may also promote the multi-disciplinary collaboration of biology, robotics, materials science, chemistry, computer science, etc.In this review, we first describe the characteristics and principles of physical intelligence in natural organisms' material, structure, and morphology. Then we introduce the purposes and related key technologies and methods of realizing bio-inspired physical intelligence of soft robots. Furthermore, we enumerate the typical applications of bio-inspired physical intelligence of soft robots. Finally, we highlight the trends and challenges of bio-inspired physical intelligence of soft robots in the future.The research on bio-inspired physical intelligence for soft robots is still in the primary stage. There are several critical questions and challenges to be addressed. First, researchers should investigate the basic principles of physical intelligence in biomechanics and then apply the basic principles to guide the development of soft robots. Second, intelligent materials need to meet the challenges of fully integrating sensing, actuation, memory, computation, and communication in soft robots. To this end, an interesting approach is to use stimulus-responsive materials as the basic building blocks to rationally design and integrate different functions into a single composite material. Third, smart structures, like mechanical metamaterials can be a promising research direction in the field of intelligent structures for soft robots. Aided by artificial intelligence and 3D printing, mechanical metamateria...

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Section: 物理智能系统集成目前越来越多的研究将智能材料与智能结构结合在一起融合力学和材料科学的前沿unclassified

Bio-inspired physical intelligence for soft robotics

Wang¹,

Xie²,

Yuan³

et al. 2022

Chin. Sci. Bull.

View full text Add to dashboard Cite

show abstract

“…A model was formulated to verify that the proposed passive rotating nozzle unit can achieve the large deformation of robot body without being affected by the torsion caused by the deformation. To date, various models of continuum robots have been proposed, ranging from continuous to discrete models [23]- [27]. In this study, a multiple rigid-link model was selected to approximate the continuum robot because the model can also reproduce the situation of torsion occurring with body deformation.…”

Section: A Model and Equation Of Motionmentioning

confidence: 99%

Realizing Large Shape Deformations of a Flying Continuum Robot With a Passive Rotating Nozzle Unit That Enlarges Jet Directions in Three-Dimensional Space

et al. 2022

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Flexible continuum robots have considerable potential for use in exploring intricate spaces, and their ability to make large body shape deformations can increase the inspection area. We previously proposed a jet-actuated flying continuum robot for extinguishing fires. The main challenge in implementing large shape deformations is accommodating the twisting of the body that results from the deformation. To address this problem, we proposed a two-dimensional passive rotating nozzle unit that can expand the directionality of net force against torsion; however, it has not yet been tested on a flying robot. In this study, we achieved the large shape deformations of a jet-actuated flying continuum robot using an improved passive rotating nozzle unit that can handle three-dimensional (3D) force. First, we developed a model of the improved nozzle unit and confirmed that the unit can increase the net force direction. Herein, the design strategy for the rotary damper to handle the instability that arises from motor limitations is discussed. The stabilized flight controller was applied to a continuum robot with the nozzle unit. Simulation results showed that the 2 m robot could perform large head bends (from 0 • to 135 • ). Although the previous fixed nozzle unit twisted by approximately 40 • , which made the extra movable range of the nozzles effectively zero, the proposed nozzle unit maintained the movable range to avoid twisting. We experimentally confirmed that the nozzle unit can expand the direction of the 3D net force, and that a large shape bending of approximately ±90 • can be achieved using a 1.6 m flying robot.INDEX TERMS Fluid jet, continuum robot, mechanism design, dynamics.

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“…Mode shape function [20], [21], euler spiral methods [22], pythagorean hodograph methods [23] were also used. Considering the mechanics of the model, mechanics modelling [24], [25], FEM based approaches [26], [27], Cosserat rod based approaches [28], [29], euler-beam mechanics based approaches [30], lumped system modelling [31], [32], absolute nodal coordinate formulation approaches (ANCF) have been used [33]. Numerical approach [34], discrete cosserat based approaches [35], [36], were also developed.…”

Section: Introductionmentioning

confidence: 99%

A Study on Position Control of a Continuum Arm Using MAML (Model-Agnostic Meta-Learning) for Adapting Different Loading Conditions

Sahoo

Chakraborty

2022

IEEE Access

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Predicting tip positions of a spring based continuum manipulator is highly challenging due to its nonlinear deformations. External loading on the tip further deteriorates the accuracy. Model-less control has shown great success in the tip positioning. However, the model less control strategies require a large data set and considerable amount of time for the training. Performance of these controllers also deteriorates with external loads. To address these problems, this paper presents a MAML(Model-Agnostic Meta-Learning) based closed loop controller for the continuum manipulators. This controller requires relatively small amount of data to achieve the state-of-art positioning accuracy. It can also adapt to the changes due to the external loads with less than 2.5 percent of the original data. Two algorithms for the offline adaptation of the known and unknown external loading are proposed. This technique is also helpful for automatic stiffness tuning of the spring based continuum manipulators. The experimental validations have been done on the simulation environment and on the real prototype. The continuum arm used for the experimentation is a tendon based non constant curvature spring-based manipulator. The average relative positioning error for the zero loading case was found to be 3.83% on the spring based prototype . The controller was successful in bringing down the relative tip positioning error of the manipulator from 5.42% to 2.7% in the simulation environment. It also showed success in bringing down the relative tip positioning error from 7.8% to 3.96% on the real prototype. Average relative tip positioning errors below 4.27% and 4.89% have been achieved in the trajectory following tasks for the known and unknown external loading cases respectively.

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Kinematic Modeling and Control of Variable Curvature Soft Continuum Robots

Cited by 74 publications

References 43 publications

Bio-inspired physical intelligence for soft robotics

Bio-inspired physical intelligence for soft robotics

Realizing Large Shape Deformations of a Flying Continuum Robot With a Passive Rotating Nozzle Unit That Enlarges Jet Directions in Three-Dimensional Space

A Study on Position Control of a Continuum Arm Using MAML (Model-Agnostic Meta-Learning) for Adapting Different Loading Conditions

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