Relevant bibliographies by topics / Modeling, control and learning of soft robots

Academic literature on the topic 'Modeling, control and learning of soft robots'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Modeling, control and learning of soft robots"

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Kim, Daekyum, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, et al. "Review of machine learning methods in soft robotics." PLOS ONE 16, no. 2 (February 18, 2021): e0246102. http://dx.doi.org/10.1371/journal.pone.0246102.

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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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Caremel, Cedric, Matthew Ishige, Tung D. Ta, and Yoshihiro Kawahara. "Echo State Network for Soft Actuator Control." Journal of Robotics and Mechatronics 34, no. 2 (April 20, 2022): 413–21. http://dx.doi.org/10.20965/jrm.2022.p0413.

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Conventional model theories are not suitable to control soft-bodied robots as deformable materials present rapidly changing behaviors. Neuromorphic electronics are now entering the field of robotics, demonstrating that a highly integrated device can mimic the fundamental properties of a sensory synaptic system, including learning and proprioception. This research work focuses on the physical implementation of a reservoir computing-based network to actuate a soft-bodied robot. More specifically, modeling the hysteresis of a shape memory alloy (SMA) using echo state networks (ESN) in real-world situations represents a novel approach to enable soft machines with task-learning. In this work, we show that not only does our ESN model enable our SMA-based robot with locomotion, but it also discovers a successful strategy to do so. Compared to standard control modeling, established either by theoretical frameworks or from experimental data, here, we gained knowledge a posteriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and informed us about what type of locomotion would work best for our robot.
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Sun, Boai, Weikun Li, Zhangyuan Wang, Yunpeng Zhu, Qu He, Xinyan Guan, Guangmin Dai, et al. "Recent Progress in Modeling and Control of Bio-Inspired Fish Robots." Journal of Marine Science and Engineering 10, no. 6 (June 2, 2022): 773. http://dx.doi.org/10.3390/jmse10060773.

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Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.
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Dai, Yicheng, Zhihao Deng, Xin Wang, and Han Yuan. "A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control." Sensors 23, no. 3 (January 22, 2023): 1272. http://dx.doi.org/10.3390/s23031272.

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Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.
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Wu, Qiuxuan, Yueqin Gu, Yancheng Li, Botao Zhang, Sergey A. Chepinskiy, Jian Wang, Anton A. Zhilenkov, Aleksandr Y. Krasnov, and Sergei Chernyi. "Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning." Information 11, no. 6 (June 8, 2020): 310. http://dx.doi.org/10.3390/info11060310.

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The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we combine the data-driven modeling method with the reinforcement learning control method to realize the position control task of robotic soft arm, the method of control strategy based on deep Q learning. In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning method is designed, which is based on the experimental data, to establish a simulation environment for control strategy training, and then applied to the real environment. Finally, it is proved by experiment that the method can effectively complete the control of the soft robot arm, which has better robustness than the traditional method.
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Jiang, Hao, Zhanchi Wang, Yusong Jin, Xiaotong Chen, Peijin Li, Yinghao Gan, Sen Lin, and Xiaoping Chen. "Hierarchical control of soft manipulators towards unstructured interactions." International Journal of Robotics Research 40, no. 1 (January 2021): 411–34. http://dx.doi.org/10.1177/0278364920979367.

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Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on [Formula: see text]-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.
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Youssef, Samuel M., MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. "Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data." Micromachines 13, no. 2 (January 29, 2022): 216. http://dx.doi.org/10.3390/mi13020216.

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Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.
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Shi, Yunde, Mingqiu Guo, Chang Hui, Shilin Li, Xiaoqiang Ji, Yuan Yang, Xiang Luo, and Dan Xia. "Learning-Based Repetitive Control of a Bowden-Cable-Actuated Exoskeleton with Frictional Hysteresis." Micromachines 13, no. 10 (October 4, 2022): 1674. http://dx.doi.org/10.3390/mi13101674.

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Bowden-cable-actuated soft exoskeleton robots are known for their light weight and flexibility of power transmission during rehabilitation training or movement assistance for humans. However, friction-induced nonlinearity of the Bowden transmission cable and gearbox backlash pose great challenges forprecise tracking control of the exoskeleton robot. In this paper, we proposed the design of a learning-based repetitive controller which could compensate for the non-linearcable friction and gearbox backlash in an iterative manner. Unlike most of the previous control schemes, the presented controller does not require apriori knowledge or intensive modeling of the friction and backlash inside the exoskeleton transmission system. Instead, it uses the iterative learning control (ILC)to adaptively update the reference trajectory so that theoutput hysteresis caused by friction and backlashis minimized. In particular, a digital phase-lead compensator wasdesigned and integrated with the ILC to address the issue of backlash delay and improve the stability and tracking performance. Experimental results showed an average of seveniterations for the convergence of learningand a 91.1% reduction in the RMS tracking error (~1.37 deg) compared withthe conventional PD control. The proposed controller design offers promising options for the realization of lightweight, wearable exoskeletons with high tracking accuracies.
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Baysal, Cabbar Veysel. "An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles." Actuators 11, no. 4 (April 16, 2022): 111. http://dx.doi.org/10.3390/act11040111.

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Rehabilitation is an area of robotics in which human–robot collaboration occurs, requiring adaptation and compliance. Pneumatic artificial muscles (PAM) are soft actuators that have built-in compliance making them usable for rehabilitation robots. Conversely, compliance arises from nonlinear characteristics and generates obstructions in modeling and controlling actions. It is a critical issue limiting the use of PAM. In this work, multi-input single-output (MISO) inverse modeling and inverse dynamics model learning approaches are combined to obtain a novel nonlinear adaptive control scheme for single PAM-actuated 1-DoF rehabilitation devices, for instance, continuous passive motion (CPM) devices. The objective of the proposed system is to bring an alternative solution to the compliant operation of PAM while performing exercise trajectories, to satisfy requirements such as larger range of motion (ROM) and adaptability to external load impedance variations. The control system combines the operation of a nonlinear autoregressive network with exogenous inputs (NARX)-based inverse dynamics estimator used as a global range controller and cascade PIDs for local position and pressure loops. Implementation results demonstrated the efficacy of the introduced method in terms of compliant operation for dynamic external load variations as well as a stable operation in case of impulsive disturbances. To summarize, a simple but efficient method is illustrated to facilitate the common use of PAM.
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Cursi, Francesco, George P. Mylonas, and Petar Kormushev. "Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool." Robotics 9, no. 3 (August 31, 2020): 68. http://dx.doi.org/10.3390/robotics9030068.

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Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.
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Dissertations / Theses on the topic "Modeling, control and learning of soft robots"

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Morales, Bieze Thor. "Contribution to the kinematic modeling and control of soft manipulators using computational mechanics." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10112/document.

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Ce travail apporte de nouvelles méthodes pour la modélisation cinématique et le contrôle de manipulateurs continus et déformables, fondées sur la méthodes des éléments finis. À la différence des manipulateurs rigides, les manipulateurs continus et déformables engendrent leurs mouvements en se déformant, c'est pourquoi la méthode proposée prend en compte les déformations mécaniques pour mieux décrire la cinématique de ce genre de robots. Cette méthode n'apporte pas de solution analytique, mais une approximation numérique, par des méthodes dérivées de la mécanique numérique. La méthodologie est appliquée à un manipulateur continu, appelé "Compact Bionic Handling Assistant (CBHA)". Une stratégie de commande en boucle fermée, fondée sur l'allocation du contrôle, est également présentée. Les modèles et contrôleurs sont validés expérimentalement
This work provides new methods for the kinematic modeling and control of soft, continuum manipulators based on the Finite Element Method. Contrary to the case of rigid manipulators, soft and continuum manipulators generate their motion by deformation, therefore, the proposed methodology accounts for the deformation mechanics to better describe the kinematics of these type of robots. This methodology does not produce analytic solutions, instead, a numerical approximation is provided by methods derived from Computational Mechanics. The methodology is applied to a continuum manipulator, namely, the Compact Bionic Handling Assistant (CBHA). A closed-loop control scheme based on control allocation is also presented. The models and controller are validated experimentally
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Lakhal, Othman. "Contribution to the modeling and control of hyper-redundant robots : application to additive manufacturing in the construction." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I061/document.

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La technologie de fabrication additive a été identifiée comme l'une des innovations numériques majeures qui a révolutionné non seulement le domaine de l'industrie, mais aussi celui de la construction. D'un point de vue de recherche, la fabrication additive reste un sujet d’actualité. C’est un procédé automatisé de dépôt de matériaux couche par couche afin d'imprimer des maisons ou des structures de petites dimensions pour un montage sur site. Dans la fabrication additive, l'étape de dépôt des matériaux est généralement suivie d'une étape de contrôle de la qualité d'impression. Cependant, le contrôle de qualité des objets imprimés ayant des surfaces funiculaires est parfois complexe à réaliser avec des robots rigides, ne pouvant atteindre des zones mortes. Dans cette thèse, un manipulateur souple et hyper-redondant a été modélisé et commandé cinématiquement, placé comme un effecteur d'un manipulateur rigide et mobile, afin d'effectuer une inspection des structures imprimées par des techniques de la fabrication additive. En effet, les manipulateurs souples peuvent fléchir et du coup suivre la forme géométrique de surfaces funiculaires. Ainsi, une approche hybride a été proposée pour modéliser la cinématique du robot souple et hyper-redondant, combinant une approche analytique pour la génération des équations cinématiques et une méthode qualitative à base des réseaux de neurones pour la résolution de ces dernières. Les performances de l'approche proposée sont validées à travers des expériences réalisées sur le robot "compact bionic handling arm" (cbha)
Additive manufacturing technology has been identified as one of the major digital innovations that has revolutionized not only industry, but also building. From a research point of view, additive manufacturing remains a very relevant topic. It is an automated process for depositing materials layer by layer to print houses or small structures for on-site assembly. In additive manufacturing processes, the deposition of materials is generally followed by a printing quality control step. However, the geometry of structures printed with funicular surfaces is sometimes complex, as robots with rigid structures cannot reach certain areas of the structure to be inspected. In this thesis, a flexible and highly redundant manipulator equipped with a camera is attached to the end-effector of a mobile manipulator robot for the quality inspection process of the printed structures. Indeed, soft manipulators can bend along their surounded 3D objects; and this inherent flexibility makes them suitable for navigation in crowded environments. As the number of controlled actuators is greater than the dimension of the workspace, this thesis can be summarized as a trajectory tracking of hyper-redundant robots. In this thesis, a hybrid approach that combines the advantages of model-based approaches and learning-based approaches is developed to model and solve the kinematics of soft and hyper-redundant manipulators. The principle is to develop mathematical models with reasonable assumptions, and to improve their accuracy through learning processes. The performance of the proposed approach is validated by performing a series of simulations and experiments applied to the compact bionic handling arm (cbha) robot
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Oliveira, Artur João Anjos. "Ultrasound Tracking and Closed-Loop Control of Magnetically-Actuated Biomimetic Soft Robot." Master's thesis, 2022. http://hdl.handle.net/10316/99395.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia
Soft robots atuados por magnetismo podem fornecer potenciais aplicações médicas e revolucionar a área de intervenções minimamente invasivas. A sua natureza mole e sem fios permite a navegação para alvos de difícil alcance do corpo humano sem danificar os tecidos circundantes. Além disso, a atuação magnética é livre de radiação, não é prejudicial para os seres humanos e elimina a necessidade de ter uma fonte de energia a bordo do robô. Apesar dos recentes desenvolvimentos no projeto e atuação deste tipo de robôs, existem alguns desafios, como localização, perceção e planeamento de caminhos, a serem superados para poderem realizar tarefas em ambientes desafiadores.O objetivo principal do projeto é alcançar o controlo de movimento em malha fechada e o planeamento de um soft robot, o Milípede, usando imagens de ultrassom. Neste estudo, integramos estratégias de localização e controlo num sistema de atuação magnética para direcionar com segurança o soft robot para um alvo. Em relação ao controlo, um controlador Proporcional Integrativo (PI) é usado para calcular as velocidades lineares e angulares para conduzir o robô pelo espaço de trabalho evitando obstáculos. Consoante as velocidades, o campo magnético correspondente é aplicado, utilizando um conjunto com seis bobinas eletromagnéticas. A localização é obtida primeiro de uma câmara a olhar para o espaço de trabalho como prova de conceito dos métodos de controlo e planeamento de movimento. Em seguida, comparamos o desempenho entre dois algoritmos de ultrassom, um geométrico e uma abordagem de aprendizagem profunda, para estimar a pose do Milípede. Por fim, o controlo de circuito fechado do soft robot é obtido, utilizando imagens de ultrassom. Os resultados mostram a possibilidade de usar os soft robots para realizar tarefas de forma autónoma em cenários clinicamente relevantes.
Untethered magnetically actuated soft robots can provide potential medical applications and revolutionize the field of minimally invasive interventions. Its soft, untethered nature allows the navigation to difficult-to-reach targets of the human body without damaging the surrounding tissues. Moreover, magnetic actuation is radiation-free, not harmful for humans and removes the need to have an on-board source of energy in the robot. Despite the recent developments in the design and actuation of soft robots, there are some challenges, such as localization, perception, and path planning, to overcome so that they can perform tasks in challenging environments.The main goal of the current project is to achieve closed-loop motion control and planning of a soft robot, the Millipede, using ultrasound imaging technique. In this study, we integrate localization and control strategies into a magnetic actuation system to safely steer the untethered soft robot to a target. Regarding the control, a Proportional Integrative (PI) controller is used to calculate the linear and angular velocities to steer the robot through the workspace while avoiding obstacles. According to the velocities, the corresponding magnetic field is applied, using a setup with six electromagnetic coils. The localization is first obtained from a top-view camera as a proof-of-concept of the motion control and planning methods. Then, we compare the performance between two ultrasound algorithms, geometric and a deep learning approach, to estimate the pose of the Millipede. Finally, the closed-loop control of the untethered soft robot is achieved using ultrasound imaging. The results show the possibility of using the soft robots to autonomously perform tasks in clinically relevant scenarios.
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Lu, Bo active 21st century. "Improving process monitoring and modeling of batch-type plasma etching tools." Thesis, 2015. http://hdl.handle.net/2152/30486.

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Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.
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Books on the topic "Modeling, control and learning of soft robots"

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Stefan, Wermter, Palm Günther, and Elshaw Mark, eds. Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, intelligent robots. Berlin: Springer, 2005.

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Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience (Lecture Notes in Computer Science). Springer, 2005.

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Metta, Giorgio. Humans and humanoids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0047.

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This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.
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Book chapters on the topic "Modeling, control and learning of soft robots"

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Huang, Weicheng, Zachary Patterson, Carmel Majidi, and M. Khalid Jawed. "Modeling Soft Swimming Robots using Discrete Elastic Rod Method." In Bioinspired Sensing, Actuation, and Control in Underwater Soft Robotic Systems, 247–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50476-2_13.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Multivariate Statistics Between Two-Observation Spaces." In Intelligent Control and Learning Systems, 31–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_3.

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AbstractAs mentioned in the previous chapter, industrial data are usually divided into two categories, process data and quality data, belonging to different measurement spaces. The vast majority of smart manufacturing problems, such as soft measurement, control, monitoring, optimization, etc., inevitably require modeling the data relationships between the two kinds of measurement variables. This chapter’s subject is to discover the correlation between the sets in different observation spaces.
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İlman, Mehmet Mert, and Pelin Yildirim Taser. "Machine Learning and Optimization Applications for Soft Robotics." In Design and Control Advances in Robotics, 13–29. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5381-0.ch002.

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Due to their adaptability, flexibility, and deformability, soft robots have been widely studied in many areas. On the other hand, soft robots have some challenges in modeling, design, and control when compared to rigid robots, since the inherent features of soft materials may create complicated behaviors owing to non-linearity and hysteresis. To address these constraints, recent research has utilized different machine learning algorithms and meta-heuristic optimization techniques. First and foremost, the study looked at current breakthroughs and applications in the field of soft robots. Studies in the field are grouped under main headings such as modelling, design, and control. Fundamental issues and developed solutions were analyzed in this manner. Machine learning and meta-heuristic optimization-oriented methods created for various applications are highlighted in particular. At the same time, it is emphasized how the problems in each of the modeling, design, and control areas impact each other.
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Zolfagharian, Ali, Mahdi Bodaghi, Pejman Heidarian, Abbas Z. Kouzani, and Akif Kaynak. "Closed-loop control of 4D-printed hydrogel soft robots." In Smart Materials in Additive Manufacturing, Volume 2 : 4D Printing Mechanics, Modeling, and Advanced Engineering Applications, 251–78. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-95430-3.00009-9.

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Rodriguez, Ricardo, Ivo Bukovsky, and Noriyasu Homma. "Potentials of Quadratic Neural Unit for Applications." In Advances in Abstract Intelligence and Soft Computing, 343–54. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2651-5.ch023.

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The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive nonlinear tools, but their nonlinear strength is naturally repaid with the local minima problem, overfitting, and high demands for application-correct neural architecture and optimization technique that often require skilled users. The QNU is the important midpoint between linear systems and highly nonlinear neural networks because the QNU is relatively very strong in nonlinear approximation; however, its optimization and performance have fast and convex-like nature, and its mathematical structure and the derivation of the learning rules is very comprehensible and efficient for implementation. These advantages of QNU are demonstrated by using real and theoretical examples.
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Mainzer, Klaus. "Challenges of Complex Systems in Cognitive and Complex Systems." In Thinking Machines and the Philosophy of Computer Science, 367–84. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61692-014-2.ch022.

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After an introduction (1) the article analyzes complex systems and the evolution of the embodied mind (2), complex systems and the innovation of embodied robotics (3), and finally discusses challenges of handling a world with increasing complexity: Large-scale networks have the same universal properties in evolution and technology (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of complex systems and future technology. We start with the distinction of symbolic and embodied AI (3.1). Embodied robotics is inspired by the evolution of life. Modern systems biology integrates the molecular, organic, human, and ecological levels of life with computational models of complex systems (3.2). Embodied robots are explained as dynamical systems (3.3). Self-organization of complex systems needs self-control of technical systems (3.4). Cellular neural networks (CNN) are an example of self-organizing complex systems offering new avenues for neurobionics (3.5). In general, technical neural networks support different kinds of learning robots (3.6). Embodied robotics aims at the development of cognitive and conscious robots (3.7).
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Conference papers on the topic "Modeling, control and learning of soft robots"

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Rajendran, Sunil Kumar, and Feitian Zhang. "Learning Based Speed Control of Soft Robotic Fish." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-8977.

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Bioinspired robotics takes advantage of biological systems in nature for morphology, action and perception to build advanced robots of compelling performance and wide application. This paper focuses on the design, modeling and control of a bioinspired robotic fish. The design utilizes a recently-developed artificial muscle named super coiled polymer for actuation and a soft material (silicone rubber) for building the robot body. The paper proposes a learning based speed control design approach for bioinspired robotic fish using model-free reinforcement learning. Based on a mathematically tractable dynamic model derived by approximating the robotic fish with a three-link robot, speed control simulation is conducted to demonstrate and validate the control design method. Exampled with a three-link reduced-order dynamic system, the proposed learning based control design approach is applicable to many and various complicated bioinspired robotic systems.
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Pawlowski, Ben, Charles W. Anderson, and Jianguo Zhao. "Dynamic Control of Soft Robots Using Reinforcement Learning." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9181.

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Abstract Soft robots made from soft materials recently attracted tremendous research owing to their unique softness compared with rigid robots, making them suitable for applications such as manipulation and locomotion. However, also due to their softness, the modeling and control of soft robots present a significant challenge because of the infinite degree of freedom. In this case, although analytic solutions can be derived for control, they are too computationally intensive for real-time application. In this paper, we aim to leverage reinforcement learning to approach the control problem. We gradually increase the complexity of the control problems to learn. We also test the effectiveness and efficiency of reinforcement learning techniques to the control of soft robots for different tasks. Simulation results show that the control commands to be computed in milliseconds, allowing effective control of soft manipulators, up to trajectory tracking.
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Luo, Ming, Mahdi Agheli, and Cagdas D. Onal. "Theoretical Modeling of a Pressure-Operated Soft Snake Robot." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35340.

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This paper addresses the theoretical modeling of the dynamics of a pressure-operated soft snake robot. An accurate dynamic model is a fundamental requirement for optimization, control, navigation, and learning algorithms for a mobile robot that can undergo serpentine locomotion. Such algorithms can be readily implemented for traditional rigid robots, but remain a challenge for nonlinear and low-bandwidth soft robotic systems. A framework to solve the 2-D modeling problem of a soft robotic snake is detailed with a general approach applicable to most pressure-operated soft robots that are developed by a modular kinematic arrangement of bending-type fluidic elastomer actuators. The model is simulated using measured physical parameters of the robot and workspace. The theoretical results are verified through a proof-of-concept comparison to locomotion experiments on a flat surface with measured frictional properties. Experimental results indicate that the proposed model describes the motion of the robot.
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Wiese, Mats, Gundula Runge-Borchert, Benjamin-Hieu Cao, and Annika Raatz. "Transfer learning for accurate modeling and control of soft actuators." In 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft). IEEE, 2021. http://dx.doi.org/10.1109/robosoft51838.2021.9479300.

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Chen, Xiaotian, Paolo Stegagno, Wei Zeng, and Chengzhi Yuan. "Localized Motion Dynamics Modeling of A Soft Robot: A Data-Driven Adaptive Learning Approach." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867191.

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Luo, Shuzhen, Merrill Edmonds, Jingang Yi, Xianlian Zhou, and Yantao Shen. "Spline-Based Modeling and Control of Soft Robots." In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2020. http://dx.doi.org/10.1109/aim43001.2020.9158917.

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Modugno, Valerio, Gerard Neumann, Elmar Rueckert, Giuseppe Oriolo, Jan Peters, and Serena Ivaldi. "Learning soft task priorities for control of redundant robots." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487137.

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Hofer, Matthias, and Raffaello D'Andrea. "Design, Modeling and Control of a Soft Robotic Arm." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594221.

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Largilliere, Frederick, Valerian Verona, Eulalie Coevoet, Mario Sanz-Lopez, Jeremie Dequidt, and Christian Duriez. "Real-time control of soft-robots using asynchronous finite element modeling." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139541.

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Yujun Lin and Weiwu Yan. "Study of soft sensor modeling based on deep learning." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172253.

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