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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Seleem, Ibrahim A. Naeem, Mohamed A. Ishii, Hiroyuki Waseda University Department of Modern Mechanical Engineering Japan Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Harbin Institute of Technology School of Mechanical and Electrical Engineering Department of Mechatronics Engineering China South Valley University Faculty of Engineering Department of Mechatronics and Robotics Engineering Egypt

ISBN: (纸本)9798350325706

Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuation methods of ESR still suffer from challenges including large dimensions, expensive, complex modeling and control, and limited payload capacity. In this paper, we introduce a novel design for a lightweight, extensible and variable stiffness soft robot-based cable-driven actuation without backbone. The design composes of one section with three segments based on semi-octagon honeycomb structure, wherein the middle segment acts like a soft spring to provide extension feature. Additionally, the top and bottom segments have identical structure with two honeycomb patterns embedded within each other to improve the stiffness of the design. A design analysis is conducted to optimize the proposed structure with respect to stress and displacement. Additionally, a shape estimation approach is utilized to get accurate inference of the prototype's shape based on data acquired from a low-cost Inertial Measurement Unit (IMU) and Motion Capture System (MCS), and constant curvature assumption. A series of experiments including workspace reach-ability, repeatability, shape approximation and payload analysis are carried out to validate the proposed design. The results show that the prototype exhibits good compression/extension ratio up to 42% and 50% relative to its normal length, respectively. Moreover, its payload capability reaches to 565 grams. © 2023 IEEE.

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Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation

arXiv

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arXiv 2023年

作者： Röstel, Lennart Pitz, Johannes Sievers, Leon Bäuml, Berthold The DLR Institute of Robotics and Mechatronics Technical University of Munich and Deggendorf Institute of Technology Germany

This paper identifies and addresses the problems with naively combining (reinforcement) learning-based controllers and state estimators for robotic in-hand manipulation. Specifically, we tackle the challenging task of purely tactile, goal-conditioned, dextrous in-hand reorientation with the hand pointing downwards. Due to the limited sensing available, many control strategies that are feasible in simulation when having full knowledge of the object's state do not allow for accurate state estimation. Hence, separately training the controller and the estimator and combining the two at test time leads to poor performance. We solve this problem by coupling the control policy to the state estimator already during training in simulation. This approach leads to more robust state estimation and overall higher performance on the task while maintaining an interpretability advantage over end-to-end policy learning. With our GPU-accelerated implementation, learning from scratch takes a median training time of only 6.5 hours on a single, low-cost GPU. In simulation experiments with the DLR-Hand II and for four significantly different object shapes, we provide an in-depth analysis of the performance of our approach. We demonstrate the successful sim2real transfer by rotating the four objects to all 24 orientations in the π/2 discretization of SO(3), which has never been achieved for such a diverse set of shapes. Finally, our method can reorient a cube consecutively to nine goals (median), which was beyond the reach of previous methods in this challenging setting. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay

arXiv

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arXiv 2023年

作者： Gus, Bartlomiej Możaryn, Jakub Institute of Automatic Control Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland

This research paper compares two neural-network-based adaptive controllers, namely the Hybrid Deep Learning Neural Network Controller (HDLNNC) and the Adaptive Model Predictive Control with Nonlinear Prediction and Linearization along the Predicted Trajectory (AMPC-NPLPT), for controlling a nonlinear object with delay. Specifically, the study investigates the effect of delay on the accuracy of the two controllers. The experimental results demonstrate that the AMPC-NPLPT approach outperforms HDLNNC regarding control accuracy for the given nonlinear object control problem. © 2023, CC BY.

关键词： Time delay

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Explicit Model Predictive Control Design based on Constrained Zonotope Propagation

Explicit Model Predictive Control Design based on Constraine...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Ali Jnadi Ramil Khusainov Simeon Nedelchev Sergei Savin Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

Zonotopes have become a widely used tool in control design, demonstrating their effectiveness in various applications ranging from state estimation to explicit model predictive control. However, the traditional use of zonotopes is limited by a set certain geometric shapes that restrict the range of state sets that can be considered. This paper proposes a novel approach to control design using constrained zonotopes that can encode arbitrary polytopes. We present a propagation scheme and an affine approximation of the exact control law for this representation, and discuss issues related to center identification for constrained zonotopes. A fast method for detecting set membership is also proposed based on lifting the constrained zonotopes into a higher dimensional space. The proposed method extends the range of shapes that can be considered in control design and offers a promising alternative to the limitations of regular zonotopes. It has the potential for wide-ranging impact in various areas of control, providing a valuable tool for solving complex control problems.

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Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality

IEEE Transactions on Field Robotics

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IEEE Transactions on Field robotics 2024年 1卷 360-393页

作者： Jongseok Lee Ribin Balachandran Konstantin Kondak Andre Coelho Marco de Stefano Matthias Humt Jianxiang Feng Tamim Asfour Rudolph Triebel Institute of Robotics and Mechatronics German Aerospace Center (DLR) Weßling Germany Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Apptronik Inc. Austin TX USA Agile Robots AG Munich Germany

This article presents a novel telemanipulation system to advance aerial manipulation in dynamic and unstructured environments. The proposed system features not only a haptic device, but also a virtual reality (VR) interface that provides real-time 3-D displays of the robot’s workspace, as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors, namely, a LiDAR, cameras, and inertial measurement units (IMUs) are utilized. For processing the acquired sensory data, pose estimation pipelines are devised for industrial objects of known and unknown geometries. We further propose an active learning (AL) pipeline in order to increase the sample efficiency of a pipeline component that relies on deep neural networks (DNNs)-based object detection. All of these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Methodologically, these results commonly suggest how an awareness of the algorithms’ own failures and uncertainty (“introspection”) can be used to tackle the encountered problems. In addition, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate more than 70 robust executions of pick-and-place, force application, and peg-in-hole tasks with the DLR cable-suspended aerial manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications. The project website can be accessed at https://***/view/vr-sam/ .

关键词： Robots Robot sensing systems Sensors Pipelines Service robots Pose estimation Visualization Three-dimensional displays Laser radar Artificial neural networks

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PROSPECTS ON APPLICATION OF ELASTIC METAMATERIALS IN ACOUSTIC EMISSION SENSING

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2023年第1期28卷

作者： Klimek, Zbigniew Paćko, Pawel Cracow branch Warsaw Poland AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Robotics and Mechatronics Krakow Poland

This paper discusses challenges, limitations and problems arising in currently employed acoustic emission testing systems, sensors and signal processing algorithms. Main factors influencing and compromising accuracy and reliability of the classically used methods are briefly analyzed. Next, metamaterials and their dynamic properties are outlined with the aim and focus on their potential in manipulating energy flow. Several applications of metamaterials in acoustic emission are presented that indicate their potential in acoustic emission. Finally, possibilities of designing a new type of sensor for acoustic emission using metamaterials is discussed. As a consequence, it is expected to increase sensitivity and accuracy of source localization with smaller number of sensors used during measurements. © 2023 The Authors.

关键词： Metamaterials

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Self-Aware AI-Based Anomaly Detection for AI IoT Enabled Smart Healthcare System

Self-Aware AI-Based Anomaly Detection for AI IoT Enabled Sma...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Yusra Shereen Muhammad Farrukh Shahid Muhammad Hassan Tanveer School of Computing FAST - NUCES Karachi Pakistan Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The Internet of Things (IoT) based smart healthcare systems can significantly enhance the quality of patient care. These systems can enable healthcare providers to monitor patient conditions in real time. IoT-based smart healthcare systems have the potential to revolutionize the way we provide care. Anomaly detection is crucial in IoT-based smart healthcare systems due to numerous reasons. It plays a dominant role in proactive health-care analysis, especially in brain diseases. In brain Magnetic Res-onance Imaging (MRI), anomaly detection helps in identifying several abnormalities, such as cysts, strokes, infections, vascular and developmental malformations, and neurological disorders like Alzheimer's and multiple sclerosis. The existing approaches of brain MRI anomaly detection, and anomaly detection in IoT-based smart healthcare systems utilizes static AI models. Conventional AI based models can be trained once and cannot tackle the changes in real time. The population health and the nature of disease consistently change, and conventional AI based models can't adapt to those changes. Conventional AI based system cannot tackle the changes caused by variations like diseases and public health and become ineffective over the time. Self-aware AI models can address these issues as they can learn and adapt to their environment. Self-aware AI-based systems have the potential to change lives through their ability to learn, adapt, and improve over time. In this paper, we proposed a novel approach to anomaly detection based on self-aware AI in IoT-based smart healthcare systems. Additionally, an algorithm for anomaly detection in MRI images, namely Self-Aware AI based Anomaly Detection (SAAD) is proposed. Our proposed system can detect anomalies in brain MRI images. We achieved 98% accuracy and 0.99 precision and recall, outperforming the existing approaches. The proposed self-aware AI based anomaly detection framework for IoT-based smart healthcare systems has the potential

关键词： Adaptation models Magnetic resonance imaging Medical services Brain modeling Real-time systems Internet of Things Artificial intelligence Public healthcare Anomaly detection Self-aware

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Evaluation of Perceived Intelligence for a Collaborative Manipulator Sharing its Workspace with a Human Operator

Evaluation of Perceived Intelligence for a Collaborative Man...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Inara Tusseyeva Artemiy Oleinikov Anara Sandygulova Matteo Rubagotti Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan

This paper studies the perception of robot intelligence for a manipulator that shares its workspace with a human operator, while the two execute independent tasks. Four different motion planning algorithms were employed to plan the robot motion in four subsequent experimental sets for 48 participants, and the order with which these algorithms were used was changed, obtaining all possible combinations. While guaranteeing safety, each of the four algorithms exhibited a different level of adaptability and reactivity, in terms of real-time motion planning abilities and of speed reduction based on heart rate feedback. We analyzed how perceived intelligence was influenced by the employed algorithm, by the order of execution and by the previous experience of the participants working with robots. In conclusion, perceived intelligence resulted being significantly lower in the first experimental set compared to the following ones, which showed a positive effect of habituation on perceived intelligence. On the other hand, neither the use of different motion planning algorithms nor the previous participants’ experience significantly influenced perceived intelligence.

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Dynamic Modelling and Task-space Control of Vine-like Soft Growing Robots

Dynamic Modelling and Task-space Control of Vine-like Soft G...

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SICE Annual Conference

作者： Haitham El-Hussieny Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

Soft-growing robots are promising for a variety of applications, such as navigating and inspecting confined and challenging environments. These robots can elongate up to tens of meters and have morphological adaptation and tip-extension capabilities. However, evaluating their performance across various design aspects or environmental conditions can be difficult due to their long manufacturing times. In this paper, we present a computationally efficient dynamics model derived using the Euler-Lagrange equation for a class of soft-growing robots known as “vine robots.” alongside a PD feedback control with gravity compensation. We have performed simulation experiments to validate both the dynamics model and the PD feedback control. The results of these experiments can provide valuable insights for future model-based feedback control design and simulation-based performance evaluation.

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Precise semi-analytical inverse kinematic solution for 7-DOF offset manipulator with arm angle optimization

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Frontiers of Mechanical Engineering 2021年第3期16卷 435-450页

作者： Boyu MA Zongwu XIE Zainan JIANG Hong LIU School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

Seven-degree-of-freedom redundant manipulators with link offset have many advantages,including obvious geometric significance and suitability for configuration *** configuration is similar to that of the experimental module manipulator(EMM)in the Chinese Space Station Remote Manipulator ***,finding the analytical solution of an EMM on the basis of arm angle parameterization is *** study proposes a high-precision,semi-analytical inverse method for ***,the analytical inverse kinematic solution is established based on joint angle ***,the analytical inverse kinematic solution for a non-offset spherical-roll-spherical(SRS)redundant manipulator is derived based on arm angle *** approximate solution of the EMM is calculated in accordance with the relationship between the joint angles of the EMM and the SRS ***,the error is corrected using a numerical method through the analytical inverse solution based on joint angle *** selecting the stride and termination condition,the precise inverse solution is computed for the EMM based on arm angle ***,case solutions confirm that this method has high precision,and the arm angle parameterization method is superior to the joint angle parameterization method in terms of parameter selection.

关键词： 7-DOF redundant manipulator inverse kinematics semi-analytical arm angle link offset

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