Journal articles on the topic 'Control and learning of soft robots'
To see the other types of publications on this topic, follow the link: Control and learning of soft robots.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Control and learning of soft robots.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Iscen, Atil, Ken Caluwaerts, Jonathan Bruce, Adrian Agogino, Vytas SunSpiral, and Kagan Tumer. "Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms." Artificial Life 21, no. 2 (May 2015): 119–40. http://dx.doi.org/10.1162/artl_a_00163.
Full textAbstract:
Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem. Instead of creating controls to handle the intricate dynamics of these robots, we can simply evolve the controls using a simulation to provide an evaluation function. In this article, we show how such a control paradigm can be applied to an emerging field within soft robotics: robots based on tensegrity structures. We take the model of the Spherical Underactuated Planetary Exploration Robot ball (SUPERball), an icosahedron tensegrity robot under production at NASA Ames Research Center, develop a rolling locomotion algorithm, and study the learned behavior using an accurate model of the SUPERball simulated in the NASA Tensegrity Robotics Toolkit. We first present the historical-average fitness-shaping algorithm for coevolutionary algorithms to speed up learning while favoring robustness over optimality. Second, we use a distributed control approach by coevolving open-loop control signals for each controller. Being simple and distributed, open-loop controllers can be readily implemented on SUPERball hardware without the need for sensor information or precise coordination. We analyze signals of different complexities and frequencies. Among the learned policies, we take one of the best and use it to analyze different aspects of the rolling gait, such as lengths, tensions, and energy consumption. We also discuss the correlation between the signals controlling different parts of the tensegrity robot.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
Hamaya, Masashi, Kazutoshi Tanaka, Felix von Drigalski, and Yoshihisa Ijiri. "Learning Control with Soft Robots: Application for Industrial Assembly." Journal of the Robotics Society of Japan 39, no. 7 (2021): 609–12. http://dx.doi.org/10.7210/jrsj.39.609.
Full text
5
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.
Full textAbstract:
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.
6
Van Meerbeek, I. M., C. M. De Sa, and R. F. Shepherd. "Soft optoelectronic sensory foams with proprioception." Science Robotics 3, no. 24 (November 28, 2018): eaau2489. http://dx.doi.org/10.1126/scirobotics.aau2489.
Full textAbstract:
In a step toward soft robot proprioception, and therefore better control, this paper presents an internally illuminated elastomer foam that has been trained to detect its own deformation through machine learning techniques. Optical fibers transmitted light into the foam and simultaneously received diffuse waves from internal reflection. The diffuse reflected light was interpreted by machine learning techniques to predict whether the foam was twisted clockwise, twisted counterclockwise, bent up, or bent down. Machine learning techniques were also used to predict the magnitude of the deformation type. On new data points, the model predicted the type of deformation with 100% accuracy and the magnitude of the deformation with a mean absolute error of 0.06°. This capability may impart soft robots with more complete proprioception, enabling them to be reliably controlled and responsive to external stimuli.
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
Pletl, Szilveszter, and Bela Lantos. "Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (March 20, 2001): 81–89. http://dx.doi.org/10.20965/jaciii.2001.p0081.
Full textAbstract:
Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.
10
KAWAMURA, KAZUHIKO, R. ALAN PETERS II, ROBERT E. BODENHEIMER, NILANJAN SARKAR, JUYI PARK, CHARLES A. CLIFTON, ALBERT W. SPRATLEY, and KIMBERLY A. HAMBUCHEN. "A PARALLEL DISTRIBUTED COGNITIVE CONTROL SYSTEM FOR A HUMANOID ROBOT." International Journal of Humanoid Robotics 01, no. 01 (March 2004): 65–93. http://dx.doi.org/10.1142/s021984360400006x.
Full textAbstract:
During the last decade, researchers at Vanderbilt have been developing a humanoid robot called the Intelligent Soft Arm Control (ISAC). This paper describes ISAC in terms of its software components and with respect to the design philosophy that has evolved over the course of its development. Central to the control system is a parallel, distributed software architecture, comprising a set of independent software objects or agents that execute as needed on standard PCs linked via Ethernet. Fundamental to the design philosophy is the direct physical interaction of the robot with people. Initially, this philosophy guided application development. Yet over time it became apparent that such interaction may be necessary for the acquisition of intelligent behaviors by an agent in a human-centered environment. Concurrent to that evolution was a shift from a programmer's high-level specification of action toward the robot's own motion acquisition of primitive behaviors through sensory-motor coordination (SMC) and task learning through cognitive control and working memory. Described is the parallel distributed cognitive control architecture and the advantages and limitations that have guided its development. Primary structures for sensing, memory, and cognition are described. Motion learning through teleoperation and fault diagnosis through system health monitoring are also covered. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.
11
Fang, Ge, Xiaomei Wang, Kui Wang, Kit-Hang Lee, Justin D. L. Ho, Hing-Choi Fu, Denny Kin Chung Fu, and Ka-Wai Kwok. "Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 1194–201. http://dx.doi.org/10.1109/lra.2019.2893691.
Full text
12
Yiğit, Tuncay, and Şadi Fuat Çankaya. "Implementation of Machine Learning Algorithms on Multi-Robot Coordination." Electronics 11, no. 11 (June 4, 2022): 1786. http://dx.doi.org/10.3390/electronics11111786.
Full textAbstract:
Occasionally, professional rescue teams encounter issues while rescuing people during earthquake collapses. One such issue is the localization of wounded people from the earthquake. Machines used by rescue teams may cause crucial issues due to misleading localization. Usually, robot technology is utilized to address this problem. Many research papers addressing rescue operations have been published in the last two decades. In the literature, there are few studies on multi-robot coordination. The systems designed with a single robot should also overcome time constraints. A sophisticated algorithm should be developed for multi-robot coordination to solve that problem. Then, a fast rescuing operation could be performed. The distinctive property of this study is that it proposes a multi-robot system using a novel heuristic bat-inspired algorithm for use in search and rescue operations. Bat-inspired techniques gained importance in soft-computing experiments. However, there are only single-robot systems for robot navigation. Another original aspect of this paper is that this heuristic algorithm is employed to coordinate the robots. The study is devised to encourage extended work related to earthquake collapse rescue operations.
13
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.
Full textAbstract:
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.
14
Yang, Jiachen, Jingfei Ni, Yang Li, Jiabao Wen, and Desheng Chen. "The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning." Sensors 22, no. 12 (June 7, 2022): 4316. http://dx.doi.org/10.3390/s22124316.
Full textAbstract:
Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.
15
Matukaitis, Mindaugas, Renaldas Urniezius, Deividas Masaitis, Lukas Zlatkus, Benas Kemesis, and Gintaras Dervinis. "Synchronized Motion Profiles for Inverse-Dynamics-Based Online Control of Three Inextensible Segments of Trunk-Type Robot Actuators." Applied Sciences 11, no. 7 (March 25, 2021): 2946. http://dx.doi.org/10.3390/app11072946.
Full textAbstract:
This study proposes a novel method for the positioning and spatial orientation control of three inextensible segments of trunk-type robots. The suggested algorithm imposes a soft constraint assumption for the end-effector’s endpoint and a mandatory constraint on its direction. Simultaneously, the algorithm by-design enforces nonholonomic features on the robot segments in the form of arcs. An approximate robot spine curve is the key to the final robot state configuration based on the given conditions. The numeric simulation showed acceptable (less than 1 s) performance for single-core processing tasks. The parametric method finds the best proximate robot state solution and represents the gray box model in addition to existing learning or black-box inverse dynamics approaches. This study also shows that a multiple inverse kinematics answer constructs a single inverse dynamics solution that defines the robot actuators’ motion profiles, synchronized in time. Finally, this text presents rotational expressions and their outlines for controlling the manipulator’s tendons.
16
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.
Full textAbstract:
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.
17
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.
Full textAbstract:
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.
18
Sola, Yoann, Gilles Le Chenadec, and Benoit Clement. "Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic." Sensors 22, no. 16 (August 14, 2022): 6072. http://dx.doi.org/10.3390/s22166072.
Full textAbstract:
The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub.
19
Billard, Aude, and Danica Kragic. "Trends and challenges in robot manipulation." Science 364, no. 6446 (June 20, 2019): eaat8414. http://dx.doi.org/10.1126/science.aat8414.
Full textAbstract:
Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.
20
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.
Full textAbstract:
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.
21
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.
Full textAbstract:
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.
22
Lin, Guojian, Wenkai Huang, Chuanshuai Hu, Junlong Xiao, Fobao Zhou, Xiaolin Zhang, Jiajian Liang, and Jiaqiao Liang. "Design and Control Strategy of Soft Robot Based on Gas–Liquid Phase Transition Actuator." Mathematics 10, no. 16 (August 10, 2022): 2847. http://dx.doi.org/10.3390/math10162847.
Full textAbstract:
In this paper, a soft robot driven by a gas–liquid phase transition actuator with a new structure is designed; The soft robot is driven by the pressure generated by electrically induced ethanol phase transition. The gas–liquid phase transition drive was found to be able to generate a larger driving force by using only low voltage. Compared with the gas drive of a traditional soft robot, gas–liquid phase transition-driven soft robot does not require a complex circuit system and a huge external energy supply air pump, making its overall structure more compact. At the same time, because of the new structure of the actuator on the soft robot, the soft robot has good gas tightness and less recovery time. A reinforcement depth learning control strategy is also added so that the soft robot with this actuator could better grip objects of different sizes and weights.
23
Graule, Moritz A., Thomas P. McCarthy, Clark B. Teeple, Justin Werfel, and Robert J. Wood. "SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots." IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 4071–78. http://dx.doi.org/10.1109/lra.2022.3149580.
Full text
24
Dong, Yuheng. "Machine Learning Amplified Control System for HASEL Actuator Soft Robot System." Journal of Physics: Conference Series 2405, no. 1 (December 1, 2022): 012026. http://dx.doi.org/10.1088/1742-6596/2405/1/012026.
Full textAbstract:
Abstract The HASEL actuator is a cutting-edge soft robot compound that is well suited for tasks in unstructured, dynamic environments and has the penitential for superiorly comfortable and smooth human-robot Interaction. However, the nonlinear relation between the input voltage, output strain of the actuators, and the difficulty of analytical modelling makes it hard to design its control software due to the various source of kinematic noises. Machine learning technics, however, which are invented to study the implicit relations in multiparameter problems that do not require pre-existing knowledge, are well suited for HASEL actuators. Traditionally, researchers consider the behavior of this time-dependent system as a sequence of consecutive statuses and use machine learning to enhance conventional algorithms that consume previous and current status and target and adjust the system using varying control input. However, HASEL actuators’ unique propriety of self-stable and negligible lag in response to input changing makes it possible to consider the spatial path of the structure as a whole and control it based on pattern matching. Introducing Recurrent Neural Networks (RNN) and multilayer perceptron (MLP), this paper presents a pattern-matching-based predictive control algorithm for the HASEL actuator system with acceptable size and high accuracy.
25
Ishige, Matthew, Takuya Umedachi, Tadahiro Taniguchi, and Yoshihiro Kawahara. "Exploring Behaviors of Caterpillar-Like Soft Robots with a Central Pattern Generator-Based Controller and Reinforcement Learning." Soft Robotics 6, no. 5 (October 1, 2019): 579–94. http://dx.doi.org/10.1089/soro.2018.0126.
Full text
26
Walker, Kathryn, and Helmut Hauser. "Evolving optimal learning strategies for robust locomotion in the spring-loaded inverted pendulum model." International Journal of Advanced Robotic Systems 16, no. 6 (November 1, 2019): 172988141988570. http://dx.doi.org/10.1177/1729881419885701.
Full textAbstract:
Robust locomotion in a wide range of environments is still beyond the capabilities of robots. In this article, we explore how exploiting the soft morphology can be used to achieve stability in the commonly used spring-loaded inverted pendulum model. We evolve adaption rules that dictate how the attack angle and stiffness of the model should be changed to achieve stability for both offline and online learning over a range of starting conditions. The best evolved rules, for both the offline and online learning, are able to find stability from a significantly wider range of starting conditions when compared to an un-adapting model. This is achieved through the interplay between adapting both the control and the soft morphological parameters. We also show how when using the optimal online rule set, the spring-loaded inverted pendulum model is able to robustly withstand changes in ground level of up to 10 m downwards step size.
27
Shahid, Asad Ali, Dario Piga, Francesco Braghin, and Loris Roveda. "Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning." Autonomous Robots 46, no. 3 (February 9, 2022): 483–98. http://dx.doi.org/10.1007/s10514-022-10034-z.
Full textAbstract:
AbstractThis paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In order to accelerate the learning process, the fine-tuning procedure is proposed that demonstrates the continuous adaptation of on-policy RL to new environments, allowing the learned policy to adapt and execute the (partially) modified task. A dense reward function is designed for the task to enable an efficient learning of the agent. A grasping task involving a Franka Emika Panda manipulator is considered as the reference task to be learned. The learned control policy is demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations. The approach is finally tested on a real Franka Emika Panda robot, showing the possibility to transfer the learned behavior from simulation. Experimental results show 100% of successful grasping tasks, making the proposed approach applicable to real applications.
28
Lyu, Chuqiao, Shuxiang Guo, Wei Zhou, Yonggan Yan, Chenguang Yang, Yue Wang, and Fanxu Meng. "A Deep-Learning-Based Guidewire Compliant Control Method for the Endovascular Surgery Robot." Micromachines 13, no. 12 (December 16, 2022): 2237. http://dx.doi.org/10.3390/mi13122237.
Full textAbstract:
Endovascular surgery is a high-risk operation with limited vision and intractable guidewires. At present, endovascular surgery robot (ESR) systems based on force feedback liberates surgeons’ operation skills, but it lacks the ability to combine force perception with vision. In this study, a deep learning-based guidewire-compliant control method (GCCM) is proposed, which guides the robot to avoid surgical risks and improve the efficiency of guidewire operation. First, a deep learning-based model called GCCM-net is built to identify whether the guidewire tip collides with the vascular wall in real time. The experimental results in a vascular phantom show that the best accuracy of GCCM-net is 94.86 ± 0.31%. Second, a real-time operational risk classification method named GCCM-strategy is proposed. When the surgical risks occur, the GCCM-strategy uses the result of GCCM-net as damping and decreases the robot’s running speed through virtual resistance. Compared with force sensors, the robot with GCCM-strategy can alleviate the problem of force position asynchrony caused by the long and soft guidewires in real-time. Experiments run by five guidewire operators show that the GCCM-strategy can reduce the average operating force by 44.0% and shorten the average operating time by 24.6%; therefore the combination of vision and force based on deep learning plays a positive role in improving the operation efficiency in ESR.
29
Ren, Qinyuan, Wenxin Zhu, Zhao Feng, and Wenyu Liang. "Learning-Based Force Control of a Surgical Robot for Tool-Soft Tissue Interaction." IEEE Robotics and Automation Letters 6, no. 4 (October 2021): 6345–52. http://dx.doi.org/10.1109/lra.2021.3093018.
Full text
30
Kim, Heonkook, Hojin Lee, and Sang Woo Kim. "Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables." Sensors 22, no. 5 (March 1, 2022): 1917. http://dx.doi.org/10.3390/s22051917.
Full textAbstract:
With the growth of factory automation, deep learning-based methods have become popular diagnostic tools because they can extract features automatically and diagnose faults under various fault conditions. Among these methods, a novelty detection approach is useful if the fault dataset is imbalanced and impossible reproduce perfectly in a laboratory. This study proposes a novelty detection-based soft fault-diagnosis method for control cables using only currents flowing through the cables. The proposed algorithm uses three-phase currents to calculate the sum and ratios of currents, which are used as inputs to the diagnosis network to detect novelties caused by soft faults. Autoencoder architecture is adopted to detect novelties and calculate anomaly scores for the inputs. Applying a moving average filter to anomaly scores, a threshold is defined, by which soft faults can be properly diagnosed under environmental disturbances. The proposed method is evaluated in 11 fault scenarios. The datasets for each scenario are collected when an industrial robot is working. To induce soft fault conditions, the conductor and its insulator in the cable are damaged gradually according to the scenarios. Experiments demonstrate that the proposed method accurately diagnoses soft faults under various operating conditions and degrees of fault severity.
31
Li, Ziyue, Xianju Yuan, and Chuyan Wang. "A review on structural development and recognition–localization methods for end-effector of fruit–vegetable picking robots." International Journal of Advanced Robotic Systems 19, no. 3 (May 1, 2022): 172988062211049. http://dx.doi.org/10.1177/17298806221104906.
Full textAbstract:
The excellent performance of fruit and vegetable picking robots is usually contributed by the reasonable structure of end-effector and recognition–localization methods with high accuracy. As a result, efforts are focused on two aspects, and diverse structures of end-effector, target recognition methods as well as their combinations are yielded continuously. A good understanding for the working principle, advantages, limitations, and the adaptability in respective fields is helpful to design picking robots. Therefore, depending on different grasping ways, separating methods, structures, materials, and driving modes, main characteristics existing in traditional schemes will be depicted firstly. According to technical routes, advantages, potential applications, and challenges, underactuated manipulators and soft manipulators representing future development are then summarized systematically. Secondly, partial recognition and localization methods are also demonstrated. Specifically, current recognition manners adopting the single-feature, multi-feature fusion and deep learning are explained in view of their advantages, limitations, and successful instances. In the field of 3D localization, active vision based on the structured light, laser scanning, time of flight, and radar is reflected through the respective applications. Also, another 3D localization method called passive vision is also evaluated by advantages, limitations, the degree of automation, reconstruction effects, and the application scenario, such as monocular vision, binocular vision, and multiocular vision. Finally portrayed from structural development, recognition, and localization methods, it is possible to develop future end-effectors for fruit and vegetable picking robots with superior characteristics containing the less driving element, rigid–flexible–bionic coupling soft manipulators, simple control program, high efficiency, low damage, low cost, high versatility, and high recognition accuracy in all-season picking tasks.
32
Martinez-Hernandez, Uriel, Benjamin Metcalfe, Tareq Assaf, Leen Jabban, James Male, and Dingguo Zhang. "Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends." Sensors 21, no. 20 (October 12, 2021): 6751. http://dx.doi.org/10.3390/s21206751.
Full textAbstract:
Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.
33
HAMMAD, ABDALLAH, SIMON X. YANG, M. TAREK ELEWA, HALA MANSOUR, and SALAH ALI. "VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS." International Journal of Computational Intelligence and Applications 10, no. 03 (September 2011): 357–75. http://dx.doi.org/10.1142/s1469026811003148.
Full textAbstract:
In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.
34
Ding, Ze Yang, Junn Yong Loo, Vishnu Monn Baskaran, Surya Girinatha Nurzaman, and Chee Pin Tan. "Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 951–57. http://dx.doi.org/10.1109/lra.2021.3056066.
Full text
35
Sun, Haoran, Tingting Fu, Yuanhuai Ling, and Chaoming He. "Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning." Sensors 21, no. 17 (September 2, 2021): 5907. http://dx.doi.org/10.3390/s21175907.
Full textAbstract:
External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for quadrupedal self-balancing, which is adaptable to multiple unpredictable scenes of external continuous disturbance. Different from conventional methods which construct analytical models which explicitly reason the balancing process, our work utilized reinforcement learning and artificial neural network to avoid incomprehensible mathematical modeling. The control policy is composed of a neural network and a Tanh Gaussian policy, which implicitly establishes the fuzzy mapping from proprioceptive signals to action commands. During the training process, the maximum-entropy method (soft actor-critic algorithm) is employed to endow the policy with powerful exploration and generalization ability. The trained policy is validated in both simulations and realistic experiments with a customized quadruped robot. The results demonstrate that the policy can be easily transferred to the real world without elaborate configurations. Moreover, although this policy is trained in merely one specific vibration condition, it demonstrates robustness under conditions that were never encountered during training.
36
Altuncu, Ismail, and Toshiro Noritsugu. "A Learning Control Application for a Pneumatic Manipulator on Impact Motion." Journal of Robotics and Mechatronics 9, no. 5 (October 20, 1997): 332–40. http://dx.doi.org/10.20965/jrm.1997.p0332.
Full textAbstract:
Pneumatic actuators are soft actuators that provide quick impacts that are not observed in other actuator types. Moreover, they absorb impact during tasks with collision. The emphasis of this study is to make use of the properties of pneumatic actuators. One of the control uses planned for the pneumatic robot link is a PD controller enhanced with a disturbance observer for maintaining reference angular velocity tracking. Another use is as a learning controller that updates the input term of the reference velocity for the velocity controller according to the impact force detected during each collision. Experiments were carried out in order to validate the velocity tracking and impact control performances of the control systems designed. Additional experiments tested the impact absorption feature of the pneumatic system. It was found that pneumatic actuators can absorb shock and vibration effects, resulting from impacts, that are undesirable in mechanical systems.
37
Lee, Kit-Hang, Denny K. C. Fu, Martin C. W. Leong, Marco Chow, Hing-Choi Fu, Kaspar Althoefer, Kam Yim Sze, Chung-Kwong Yeung, and Ka-Wai Kwok. "Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation." Soft Robotics 4, no. 4 (December 2017): 324–37. http://dx.doi.org/10.1089/soro.2016.0065.
Full text
38
Xiloyannis, Michele, Leonardo Cappello, Khanh D. Binh, Chris W. Antuvan, and Lorenzo Masia. "Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living." Journal of Rehabilitation and Assistive Technologies Engineering 4 (January 2017): 205566831668031. http://dx.doi.org/10.1177/2055668316680315.
Full textAbstract:
The development of a portable assistive device to aid patients affected by neuromuscular disorders has been the ultimate goal of assistive robots since the late 1960s. Despite significant advances in recent decades, traditional rigid exoskeletons are constrained by limited portability, safety, ergonomics, autonomy and, most of all, cost. In this study, we present the design and control of a soft, textile-based exosuit for assisting elbow flexion/extension and hand open/close. We describe a model-based design, characterisation and testing of two independent actuator modules for the elbow and hand, respectively. Both actuators drive a set of artificial tendons, routed through the exosuit along specific load paths, that apply torques to the human joints by means of anchor points. Key features in our design are under-actuation and the use of electromagnetic clutches to unload the motors during static posture. These two aspects, along with the use of 3D printed components and off-the-shelf fabric materials, contribute to cut down the power requirements, mass and overall cost of the system, making it a more likely candidate for daily use and enlarging its target population. Low-level control is accomplished by a computationally efficient machine learning algorithm that derives the system’s model from sensory data, ensuring high tracking accuracy despite the uncertainties deriving from its soft architecture. The resulting system is a low-profile, low-cost and wearable exosuit designed to intuitively assist the wearer in activities of daily living.
39
Khan, Said G., Guido Herrmann, Mubarak Al Grafi, Tony Pipe, and Chris Melhuish. "Compliance Control and Human–Robot Interaction: Part 1 — Survey." International Journal of Humanoid Robotics 11, no. 03 (September 2014): 1430001. http://dx.doi.org/10.1142/s0219843614300013.
Full textAbstract:
Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents a review of various compliance control techniques. The paper is aimed to provide a good background knowledge for new researchers and highlight the current hot issues in compliance control research. Active compliance, passive compliance, adaptive and reinforcement learning-based compliance control techniques are discussed. This paper provides a comprehensive literature survey of compliance control keeping in view physical human robot interaction (pHRI) e.g., passing an object, such as a cup, between a human and a robot. Compliance control may eventually provide an immediate and effective layer of safety by avoiding pushing, pulling or clamping in pHRI. Emerging areas such as soft robotics, which exploit the deformability of biomaterial as well as hybrid approaches which combine active and passive compliance are also highlighted.
40
Wu, Yang, Min Yang, and Jiancheng Zhang. "Open-Closed-Loop Iterative Learning Control with the System Correction Term for the Human Soft Tissue Welding Robot in Medicine." Mathematical Problems in Engineering 2020 (December 22, 2020): 1–9. http://dx.doi.org/10.1155/2020/2458318.
Full textAbstract:
By combining manual welders (with intelligence and versatility) and automatic welding systems (with accuracy and consistency), an intelligent welding system for human soft tissue welding can be developed in medicine. This paper presents a data-correction control approach to human welder intelligence, which can be used to control the automated human soft tissue welding process. Human soft tissue welding can preconnect the excised tissue, and the shape of the tissue at the junction ensures the recovery of the operative organ function. This welding technology has the advantages of rapid operation, minimal tissue damage, no need for suture materials, faster recovery of the mechanism and properties of the living tissue, and the maintenance of the function of the organs. Model of the welding system is identified from the data; an open-closed-loop iterative learning control algorithm is then proposed to improve the tracking accuracy of the system. The algorithm uses the tracking error of current and previous to update the control law. Meanwhile, to further improve the accuracy under the conditions of external interference, a system correction term is added to the proposed ILC algorithm, which can be adjusted according to the system’s errors and output and improve the capability of the target tracking greatly. A detailed convergence analysis for the ILC law has been given. Simulation results verify the feasibility and effectiveness of the proposed method for GTAW control tasks.
41
Mamaeva, Alla, Andrey Shevchenko, Sergey Ulyanov, Maria Feng, and Kazuo Yamafuji. "Human being emotion in cognitive intelligent robotic control. Pt.I: Quantum/ soft computing approach." System Analysis in Science and Education, no. 4 (2019) (2019): 87–131. http://dx.doi.org/10.37005/2071-9612-2019-4-87-131.
Full textAbstract:
The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular case, the aim of this part is to demonstrate the possibility of classifying the mental states in on line of a human being operator with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
42
Taufik, Muhamad. "STRATEGIC ROLE OF ISLAMIC RELIGIOUS EDUCATION IN STRENGTHENING CHARACTER EDUCATION IN THE ERA OF INDUSTRIAL REVOLUTION 4.0." Jurnal Ilmiah Islam Futura 20, no. 1 (February 29, 2020): 86. http://dx.doi.org/10.22373/jiif.v20i1.5797.
Full textAbstract:
Islamic Education plays a strategic role in the strengthening of character education in the era of the industrial revolution 4.0. Trends in automation, data exchange, Artificial intelligence, internet of things, 3D printing, robots and intelligent machines are massively replacing human labor. Disclosure of information access enables people receive a variety of information and can affect even change the character, way of thinking and behaving. Here, the role of Islamic Education as a facilitator who runs the cultural function and ideal function as a control value and steers the development of society. Strengthened with social reconstruction curriculum so that learning is more focused on the problems-problems encountered in the community so that students are able to adapt to the development of the modern world and highly competitive yet-holistic integrative religious character so as to fortify the morale of the nation's poor from the impact of globalization. Strengthening the Character Education through Islamic education has a significant contribution to the ideals of national education as a form of educational investment in creating a golden generation in the era of the global arena by establishing a balance hard skills and soft skills, which in turn will create a community culture of learning (learning society).
43
Fang, Ge, Marco C. K. Chow, Justin D. L. Ho, Zhuoliang He, Kui Wang, T. C. Ng, James K. H. Tsoi, et al. "Soft robotic manipulator for intraoperative MRI-guided transoral laser microsurgery." Science Robotics 6, no. 57 (August 18, 2021): eabg5575. http://dx.doi.org/10.1126/scirobotics.abg5575.
Full textAbstract:
Magnetic resonance (MR) imaging (MRI) provides compelling features for the guidance of interventional procedures, including high-contrast soft tissue imaging, detailed visualization of physiological changes, and thermometry. Laser-based tumor ablation stands to benefit greatly from MRI guidance because 3D resection margins alongside thermal distributions can be evaluated in real time to protect critical structures while ensuring adequate resection margins. However, few studies have investigated the use of projection-based lasers like those for transoral laser microsurgery, potentially because dexterous laser steering is required at the ablation site, raising substantial challenges in the confined MRI bore and its strong magnetic field. Here, we propose an MR-safe soft robotic system for MRI-guided transoral laser microsurgery. Owing to its miniature size (Ø12 × 100 mm), inherent compliance, and five degrees of freedom, the soft robot ensures zero electromagnetic interference with MRI and enables safe and dexterous operation within the confined oral and pharyngeal cavities. The laser manipulator is rapidly fabricated with hybrid soft and hard structures and is powered by microvolume (<0.004 milliter) fluid flow to enable laser steering with enhanced stiffness and lowered hysteresis. A learning-based controller accommodates the inherent nonlinear robot actuation, which was validated with laser path–following tests. Submillimeter laser steering accuracy was demonstrated with a mean error < 0.20 mm. MRI compatibility testing demonstrated zero observable image artifacts during robot operation. Ex vivo tissue ablation and a cadaveric head-and-neck trial were carried out under MRI, where we employed MR thermometry to monitor the tissue ablation margin and thermal diffusion intraoperatively.
44
Ansari, Yasmin, Mariangela Manti, Egidio Falotico, Yoan Mollard, Matteo Cianchetti, and Cecilia Laschi. "Towards the development of a soft manipulator as an assistive robot for personal care of elderly people." International Journal of Advanced Robotic Systems 14, no. 2 (March 1, 2017): 172988141668713. http://dx.doi.org/10.1177/1729881416687132.
Full textAbstract:
Manipulators based on soft robotic technologies exhibit compliance and dexterity which ensures safe human–robot interaction. This article is a novel attempt at exploiting these desirable properties to develop a manipulator for an assistive application, in particular, a shower arm to assist the elderly in the bathing task. The overall vision for the soft manipulator is to concatenate three modules in a serial manner such that (i) the proximal segment is made up of cable-based actuation to compensate for gravitational effects and (ii) the central and distal segments are made up of hybrid actuation to autonomously reach delicate body parts to perform the main tasks related to bathing. The role of the latter modules is crucial to the application of the system in the bathing task; however, it is a nontrivial challenge to develop a robust and controllable hybrid actuated system with advanced manipulation capabilities and hence, the focus of this article. We first introduce our design and experimentally characterize its functionalities, which include elongation, shortening, omnidirectional bending. Next, we propose a control concept capable of solving the inverse kinetics problem using multiagent reinforcement learning to exploit these functionalities despite high dimensionality and redundancy. We demonstrate the effectiveness of the design and control of this module by demonstrating an open-loop task space control where it successfully moves through an asymmetric 3-D trajectory sampled at 12 points with an average reaching accuracy of 0.79 cm ± 0.18 cm. Our quantitative experimental results present a promising step toward the development of the soft manipulator eventually contributing to the advancement of soft robotics.
45
Ji, Qinglei, Shuo Fu, Kaige Tan, Seshagopalan Thorapalli Muralidharan, Karin Lagrelius, David Danelia, Georgios Andrikopoulos, Xi Vincent Wang, Lihui Wang, and Lei Feng. "Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning." Robotics and Computer-Integrated Manufacturing 78 (December 2022): 102382. http://dx.doi.org/10.1016/j.rcim.2022.102382.
Full text
46
Biswas, Swarnava, Debajit Sen, Dinesh Bhatia, and Moumita Mukherjee. "COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification." International Journal of Statistics in Medical Research 10 (November 15, 2021): 146–60. http://dx.doi.org/10.6000/1929-6029.2021.10.14.
Full textAbstract:
Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem.
47
Gu, Yili, Zhiqiang Li, Zhen Zhang, Jun Li, and Liqing Chen. "Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm." Sensors 20, no. 3 (January 31, 2020): 797. http://dx.doi.org/10.3390/s20030797.
Full textAbstract:
Due to the narrow row spacing of corn, the lack of light in the field caused by the blocking of branches, leaves and weeds in the middle and late stages of corn growth, it is generally difficult for machinery to move between rows and also impossible to observe the corn growth in real time. To solve the problem, a robot for corn interlines information collection thus is designed. First, the mathematical model of the robot is established using the designed control system. Second, an improved convolutional neural network model is proposed for training and learning, and the driving path is fitted by detecting and identifying corn rhizomes. Next, a multi-body dynamics simulation software, RecurDyn/track, is used to establish a dynamic model of the robot movement in soft soil conditions, and a control system is developed in MATLAB/SIMULINK for joint simulation experiments. Simulation results show that the method for controlling a sliding-mode variable structure can achieve better control results. Finally, experiments on the ground and in a simulated field environment show that the robot for field information collection based on the method developed runs stably and shows little deviation. The robot can be well applied for field plant protection, the control of corn diseases and insect pests, and the realization of human–machine separation.
48
Nakazawa, Masaru. "Special Issue on Handling of Flexible Object." Journal of Robotics and Mechatronics 10, no. 3 (June 20, 1998): 167–69. http://dx.doi.org/10.20965/jrm.1998.p0167.
Full textAbstract:
It is difficult to introduce highly versatile automation using robots to handling deformable objects such as thread, cloth, wire, long beams, and thin plates in plant production processes, compared to the handling of rigid objects. Office equipment handles deformable objects such as paper and plastic. Problems unique to these objects is caused by speeding up such equipment and demand for upgrading its accuracy. In agriculture and medical care, automatic, intelligent handling of deformable objects such as fruit and animals has long been desired and practical systems sought. Deformable objects whose handling should be versatiley and accurately automated are classified into two groups based on handling: (A) Flexible, mostly thin, fine objects capable of elastic deformation (B) Soft objects easily crushed, such as soft fruits or animals The problem in handling the first group is controlling object deformation of an infinite degree of freedom with a finite number of manipulated variables. In contrast, a significant problem in handling the second group is often how to handle them without exerting excessive stress and how to handle them safely and reliably. The handling of these two groups differ greatly in mechanics and control theory, and this special issue focuses on the first group — flexible objects — mechanical collection and transport studies, control, and software. Recent studies on their handling are classified into four groups for convenience based on handled objects and types of handling task: (a) Control of deformation, internal force, and vibration or path planning of flexible objects (mainly thin plates and beams) using single or multiple manipulators. (b) Task understanding in insertion of elastic into rigid parts and vice versa, and the study of human skills to help robots accomplish these task. (c) Approaches on improved accuracy, intelligent control, and vibration damping in handling and transfer of sheets and strings with low flexural rigidity, represented by paper or wire. (d) Strategies for grasping and unfolding sheets such as cloth whose flexural rigidity is almost nil. For (a), studies are active on deformation control by two robot hands attempting to grasp cloth. 1-3) In the automobile industry, so-called flexible fixtureless assembly systems are advancing in which two robots process or assemble parts in mid-air without a fixed table to reduce lead time and cost. These systems are mostly developed assuming handled parts are rigid. Nguyen et al. work assuming parts such as sheet metal whose deformation must be taken into consideration.1) Nakagaki et al. propose form estimation that considers even plastic deformation in wire handling by robots, in connection with the development of robots for electric wire installation.4) Many studies cover flexible wire as elastic beams,3-9) but comparatively few focus on bending deformation of thin plates. This special edition includes a paper by Kosuge et al. on thin-plate deformation control. Vibration control of grasped objects becomes important as speed increases. Matsuno kindly contributed his paper on optimum path planning in elastic plate handling. In controlling the deformation of elastic bodies, the mechanics of objects handled is often unknown. This special issue features a paper by Kojima et al. on an approach to this problem by adaptive feed-forward control. For (b), we consider three cases: (1) A cylindrical rigid body inserted into a hole on an elastic plate. (2) An elastic bar inserted into a hole on a rigid body. (3) A tubular elastic body put on a cylindrical rigid body. This special issue carries papers on these problems by Brata et al., Matsuno et al., and Hirai. For (2), a paper by Nakagaki et al.10) covers electric wire installation. For (3), the paper by Shima et al.11) covers insertion of a rigid axis into an elastic hose. Robot skill acquisition is an important issue in robotics in general, and the above papers should prove highly interesting and information because they treat studies by comparing robot and human skills in accomplishing work and acquiring concrete skills knowledge. For (c), attempts are made to theoretically analyze sheet handling mechanisms and control developed based on trial and error, and to structure design theory based on such analysis. These attempts are related to the increased accuracy and speed and enhanced intelligence of sheet-handling office automation equipment such as printers, facsimile machines, copiers, and automated teller machines. Yoshida et al. conducted a series of studies on the effects of guides forming paper feed paths and of inertia force of paper by approximating sheets with a chain of discrete masses and springs.12-14) This special edition also features a study on sheet sticking and jamming. Okuna et al. handles a system of similar nature, mechanical studying the form of paper guides.15) Introducing mechanisms to control the positioning of sheets is effective in raising sheet transfer accuracy. Feedback control that regulates feed roller skew angle as a manipulated variable is proposed.16) Increased reliability in separating single sheets from stacked effectively reduces the malfunction rate in sheet-handling equipment. Ways of optimizing the form of sheet-separation rollers17) and estimating frictional force between separation gates and sheets 18) are also proposed. This special issue contains a proposal by Nakazawa et al. of a mechanism that uses reactive sheet buckling force, made in connection with development of a newspaper page turner for the disabled as technology for separating single sheets. Dry frictional force is most widely used for transporting sheets, but is not stable and may even act as an obstacle to improving accuracy. Niino et al. propose a sheet transfer mechanism that uses electrostatic force.19) For improving the accuracy of flexible wire transmission, this special issue carries a study on transporting flexible thin wire through tension control at multiple points, from a study by Morimitsu et al. on optical fiber installation. The thickness of wire used in equipment is becoming increasingly slim and flexible, along with the equipment it is used in. Tension control in the production process is an important factor in the manufacture of such thin wire. Production efficiency constantly calls for increased transfer speed. It has thus become important to estimate air resistance and inertia and to measure and control the tension of running wire. Studies20,21) by Batra, Fraser, et al. which deal the motion of string in the spinning process provide good examples for learning analytical techniques for air drag and inertia. In string vibration where inertia dominates, attempts are made to control vibration by boundary shaking22,23) and feed-forward/back control.24) For (d), highly versatile robots for handling cloth are being developed, and the software technology for automatic cloth selection and unfolding by robot hands is a popular topic.25-27) Ono et al. comment on the nature of problems in developing intelligent systems for handling cloth and similar objects whose bending rigidity is low and which readily fold and overlap—a paper that will prove a good reference in basic approaches in this field. Mechanical analyses are indispensable to studies on (a) through (c). In contrast, information technology such as characteristic variable measurement, image processing, and discrimination, rather than mechanical analyses, play an important roles in studies on (d). This special issue features a study by Hamashima, Uraya et al. on cloth unfolding as an example of such studies. Studies up to now largely assumed that properties of grasped objects did not change environmental influences such as temperature and humidity. Such influence is often, however, a major factor in handling fiber thread and cloth. This special issue has a paper contributed by Taylor, who studies handling method to prevent influence by such environmental factors. The objective of this special issue will have been achieved if it aids those studying the handling of flexible objects by providing approaches and methodologies of researchers whose target objects differ and if it aids those planning to take up study in this field by providing a general view of this field. References: 1) Nguyen, W. and Mills, J., ""Multi-Robot Control For Plexible Fixtureless Assembly of Flexible Sheet Metal Auto Body Parts,"" Proceedings of the 1996 IEEE International Conference on Robotics and Automation, 2340-2345, (1996). 2) Sun, D. and Shi, X. and Liu, Y., ""Modeling and Cooperation of Two-Arm Robotic System Manipulating a Deformable Object,"" Proceedings of the 1996 IEEE International Conference on Robotics and Automation, 2346-2351, (1996). 3) Kosuge, K., Sakaki, M., Kanitani, K., Yoshida, H. and Fukuda, T., ""Manipulation of a Flexible Object by Dual Manipulators,"" IEEE International Conference on Robotics and Automation, 318-323, (1995). 4) Nakagaki, H., Kitagaki, K., Ogasawara, T. and Tukune H., ""Handling of a Flexible Wire -Detecting a Deformed Shape of the Wire by Vision and a Force Sensor,"" Annual Conference on Robotics and Mechatronics (ROBOMEC'96), 207-210, (1996). 5) Wakamatsu, H., Hirai, S. and Iwata, K., ""Static Analysis of Deformable Object Grasping Based on Bounded Force Closure,"" Trans. of JSML, 84-618 (C), 508-515, (1998). 6) Katoh, R. and Fujmoto, T., ""Study on Deformation of Elastic Object By Manipulator -Path Planning of End -Effector-,"" J. of the Robotics Society of Japan, 13-1, 157-160, (1995). 7) Yukawa, T., Uohiyama, M. and Inooka, M., ""Stability of Control System in Handling a Flexible Object by Rigid Arm Robots,"" JSME Annual Conference on Robotics and Mechatronics (ROBOMEC'95), 169-172, (1995). 8) Yukawa, T., Uohiyama, M. and Cbinata, G., ""Handling of a Vibrating Flexible Structure by a Robot,"" Trans. JSME, 61-583, 938-943, (1995). 9) Sun, D. and Liu, Y., ""Modeling and Impedance Control of a Two-Manipulator System Handling a Flexible Beam,"" Trans. of the ASME, 119, 736-742, (1997). 10) Nakagaki, H., Kitagaki, K. and Tukune, H., ""Contact Motion in Inserting a Flexible Wire into a Hole,"" Annual Conference on Robotics and Mechatronics (ROBOMEC'95), 175-178, (1995). 11) Shimaji, S., Brata, A. and Hattori, H., ""Robot Skill in Assembling a Cylinder into an Elastic Hose,"" Annual Conference on Robotics and Mechatronics (ROBOMEC'95), 752-755, (1995). 12) Yoshida, K. and Kawauchi, M., ""The Analysis of Deformation and Behavior of Flexible Materials (1st Reprt, Study of Spring-Mass Beam Model of the Sheet,"" Trans. of JSME, 58-552, 1474-1480, (1992). 13) Yoshida, K., ""Analysis of Deformation and Behavior of Flexible Materials (2nd Report, Static Analysis for Deformation of the Sheet in the Space Formed by Guide Plates),"" Trans. JSME, 60-570, 501-507, (1994). 14) Yoshida, K., ""Dynamic Analysis of Sheet Defofmation Using Spring-Mass-Beam Model,"" Trans. JSME, 63-615, 3926-3932 (1997). 15) Okuna, K., Nishigaito, T. and Shina, Y., ""Analysis of Paper Deformation Considering Guide Friction (Improvement of Paper Path for Paper-Feeding Mechanism),"" Trans. JSME, 60-575, 2279-2284, (1994). 16) Fujimura, H. and Ono, K., ""Analysis of Paper Motion Driven by Skew-Roll Paper Feeding System,"" Trans. JSME, 62-596, 1354-1360, (1996). 17) Shima, Y., Hattori, S., Kobayashi, Y. and Ukai, M., ""Optimum of Gate-Roller Shape in Paper Isolating Methods,"" Conference of Information, Intelligence and Precision Equipment (IIP'96), 61-62, (1996). 18) Suzuki, Y, Hattori, S., Shima, Y. and Ukai, M., ""Contact Analysis of Paper in Gate-Roller Handling Method"", Conference on Information, Intelligence and Precision Equipment (IIP'95), 19-20, (1995). 19) Niino, T., Egawa, S. and Higuchi, T., ""An Electrostatic Paper Feeder,"" J. of the Japan Society for Precision Engineering, 60-12,1761-1765, (1994). 20) Batra, S., Ghosh, T. and Zeidman, M., ""An Integrated Approach to Dynamic Analysis of the Ring Spinning Process , PartII: With Air Drag,"" Textile Research Journal, 59, 416-424, (1989). 21) Fraser, W., Ghosh, T. and Batra, S., ""On Unwinding Yarn from a Cylindrical Package,"" Proceedings of Royal Society of London, A, 436, 479-438, (1992). 22) Jacob, S., ""Control of Vibrating String Using Impedance Matching,"" Proceedings of the American Control Conference (San Francisco),468-472, (1993). 23) Lee, S. and Mote, C., ""Vibration Control of an Axially Moving String by Boundary Control,"" Trans. of the ASME, J. of Dynamic Systems, Measurement, and Control, 118, 66-74, (1996). 24) Ying, S. and Tan, C., ""Active Vibration Control of the Axially Moving String Using Space Feedforward and Feedback Controllers,"" Trans. ASME, J. of Vibration and Acoustics, 118, 306-312, (1996). 25) Ono, E., Ichijo, H. and Aisaka, N., ""Flexible Robotic Hand for Handling Fabric Pieces in Garment Manufacture,"" International Journal of Clothing Science and Technology, 4-5,18-23, (1992). 26) Paraschidis, K., Fahantidis, N, Petridis, V., Doulgeri, Z., Petrou, L. and Hasapis, G, ""A Robotic System for Handling Textile and Non Rigid Flat Materials,"" Computers in Industry, 26, 303-313, (1995). 27) Fahantidis, N., Paraschidis, K, Petridis, V., Doulgeri, Z., Petrou, L. and Hasapis, G., ""Robot Handling of Flat Textile Materials,"" IEEE Robotics & Automation Magazine, 4-1, 34-41, (1997).
49
Della Santina, Cosimo, Robert K. Katzschmann, Antonio Bicchi, and Daniela Rus. "Editorial: Soft Robotic Modeling and Control: Bringing Together Articulated Soft Robots and Soft-Bodied Robots." International Journal of Robotics Research 40, no. 1 (January 2021): 3–6. http://dx.doi.org/10.1177/0278364921998088.
Full text
50
Bongard, Josh C. "Evolving Soft Robots." Soft Robotics 3, no. 2 (June 2016): 43–44. http://dx.doi.org/10.1089/soro.2016.29008.jcb.
Full text