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arXiv 2021年

作者： Kulkarni, Abhishek N. Han, Shuo Leslie, Nandi O. Kamhoua, Charles A. Fu, Jie The Robotics Engineering Program Dept. of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60607 United States Raytheon Technologies U.S. Army Research Laboratory

We consider the probabilistic planning problem where the agent (called Player 1, or P1) can jointly plan the control actions and sensor queries in a sensor network and an attacker (called player 2, or P2) can carry out attacks on the sensors. We model such an adversarial interaction using a formal model-a reachability game with partially controllable observation functions. The main contribution of this paper is to assess the cost of P1's unawareness: Suppose P1 misinterprets the sensor failures as probabilistic node failures due to unreliable network communication, and P2 is aware of P1's misinterpretation in addition to her partial observability. Then, from which states can P2 carry out sensor attacks to ensure, with probability one, that P1 will not be able to complete her reachability task even though, due to misinterpretation, P1 believes that she can almost-surely achieve her task. We develop an algorithm to solve the almost-sure winning sensor-attack strategy given P1's observation-based strategy. Our attack analysis could be used for attack detection in wireless communication networks and the design of provably secured attack-aware sensor allocation in decision-theoretic models for cyber-physical systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal methods

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Three-Dimensional Visual Mapping of Underwater Ship Hull Surface using View-based Piecewise-Planar Measurements

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IFAC-PapersOnLine 2019年第21期52卷 384-389页

作者： Seonghun Hong Jinwhan Kim Robotics Program KAIST Daejeon Korea Department of Mechanical Engineering KAIST Daejeon Korea

Visual inspection of underwater ship hulls using unmanned underwater vehicles needs to be conducted at very close range to the target surface because of the limited visibility conditions in underwater environments due to light attenuation, scattering, and turbidity. These environmental challenges result in ineffective photometric and geometric information in hull surface images, and thus, the performance of conventional 3D reconstruction techniques is often not satisfactory. This paper presents a visual mapping method for 3D reconstruction of underwater ship hull surface using a monocular camera as a primary mapping sensor. The main idea of the proposed approach is to model the moderately curved hull surface as a combination of piecewise-planar panels, and to generate a global map by aligning the images in a 2D reference frame and correcting them appropriately to reflect the information of perspective projections of the 3D panels. Experimental results are shown to demonstrate the feasibility of the proposed method using a dataset obtained in a field experiment with a full-scale ship in a real sea environment.

关键词： visual mapping 3D reconstruction computer vision autonomous underwater vehicle visual inspection

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Toward “S”-Shaped 3D-Printed Soft Robotic Guidewires for Pediatric Patent Ductus Arteriosus Endovascular Interventions

Toward “S”-Shaped 3D-Printed Soft Robotic Guidewires for P...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Adira Colton Declan Fitzgerald Sunandita Sarker Noah Barnes Dheeraj Gandhi Miroslaw Janowski Jeremy D. Brown Joshua Kanter Laura Olivieri Mark Fuge Axel Krieger Ryan D. Sochol Department of Mechanical Engineering University of Maryland College Park MD USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Program in Image Guided Neurointerventions Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine Baltimore MD USA Children's National Heart Institute Children's National Hospital Washington DC USA Department of Pediatrics University of Pittsburgh School of Medicine Pittsburgh PA USA Maryland Robotics Center University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA

Patent Ductus Arteriosus (PDA) is a heart condition in which the ductus arteriosus-a blood vessel connecting the pulmonary artery to the aorta in a fetus-fails to undergo closure after birth. A PDA can be an important factor in neonates born with severe congenital heart disease (CHD) or born prematurely. With the advent of new intravascular stent technologies, treatments based on ductus arteriosus stenting can now be completed in many cases; however, difficulties remain in accessing the ductus arteriosus in small babies successfully using current guidewire-catheter systems. Recent developments for soft robotic endovascular instruments that leverage control schemes hold distinctive potential for addressing these access challenges, but such technologies are not yet at the sizes required for navigating neonatal vasculature safely and efficiently. In an effort to meet this clinical need, this work presents an approach for 3D printing 1.5 French (Fr) soft robotic guidewires that transition from straight to “S”-shaped configurations under the application of fluidic (e.g., pneumatic or hydraulic) loading. Two distinct dual-opposing segmented soft actuators, including a symmetric and asymmetric system design (both with heights of 2.5 mm), were 3D printed onto 1.1 Fr capillaries in 35–60 minutes via “Two-Photon Direct Laser Writing (DLW)”. Experimental results revealed that both designs not only withstood pressures of up to 550 kPa, but also exhibited increased opposing bending deformations-corresponding to decreased radii of curvature-with increasing applied pressure. In combination, this study serves as a critical foundation for next-generation fluidically actuated soft robotic guidewire-catheter systems for PDA interventions.

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SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb

SkinSource: A Data-Driven Toolbox for Predicting Touch-Elici...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Neeli Tummala Gregory Reardon Simone Fani Dustin Goetz Matteo Bianchi Yon Visell the Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA the Media Arts and Technology Program University of California Santa Barbara Santa Barbara CA USA the School of Biological and Health Systems Engineering Arizona State University Tempe AZ USA the Soft Robotics for Human Cooperation and Rehabilitation Lab Instituto Italiano di Tecnologia Geneva GE Italy the Department of Mechanical Engineering University of California Santa Barbara Santa Barbara CA USA the Department of Information Engineering University of Pisa PI Italy the Department of Biological Engineering University of California Santa Barbara Santa Barbara CA USA

Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.

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Reducing Uncertainty in Pose Estimation under Complex Contacts via Force Forecast

Reducing Uncertainty in Pose Estimation under Complex Contac...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Huitan Mao Jing Xiao Department of Computer Science University of North Carolina Charlotte NC USA Robotics Engineering Program and Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

How to reduce uncertainty in object pose estimation under complex contacts is crucial to autonomous robotic manipulation and assembly. In this paper, we introduce an approach through forecasting contact force from simulated complex contacts with calibration based on real force sensing. A constraint-based haptic simulation algorithm is used with sphere-tree representation of contacting objects to compute contact poses and forces, and through matching the computed forces to measured real force data via a regression model, the least-uncertain estimate of the relative contact pose is obtained. Our approach can handle multi-region complex contacts and does not make any assumption about contact types or contact locations. It also does not have restriction on object shapes. We have applied the force forecast approach to reducing uncertainty in estimating object poses in challenging peg-in-hole robotic assembly tasks and demonstrate the effectiveness of the approach by successful completion of contact-rich two-pin and three-pin real peg-in-hole assembly tasks with complex shapes of pins and holes.

关键词： Force Uncertainty Robot sensing systems Task analysis Calibration

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Development of automatic rice plant pest detection system based on convolutional neural network

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AIP Conference Proceedings 2023年第1期2482卷

作者： Matthew Sjarah Winda Astuti Lie Zener Ari Fadli 1)Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480 2)Electrical Departement Faculty of Engineering Jenderal Soedirman University Purwokerto Jawa Tengah Indonesia 53122

Rice is the most important food crop in Indonesia, one of the plants that is quite significant and as a daily staple food for the people of Indonesia. The Indonesia government through the ministry of Agriculture tries to maintain the rice production by maintaining of the rice field. One the challenging situation that can involve the rice production is pest. Pests became one of the reasons which can reduce rice production. The decrease in rice production due to pest attack is an important problem in rice plant care. In this work, a pest detection system in rice plants developed using an intelligent system technique. The system involved image processing and intelligent technique. The system recognizes the kind of pests of the rice plant based on the feature of the image of the pets. Rice plant pest detection systems based on the pest image proceed by image procession technique and Convolution Neural Network (CNN). The system is working properly, since it resulting the training and testing accuracy of 99% and 90%, respectively.

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Hardware-in-the-Loop Simulation Testbed for Three-Axis Earth's Magnetic Field Generation Based on 2.4- Meter Square Helmholtz Coils

Hardware-in-the-Loop Simulation Testbed for Three-Axis Earth...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Jormpon Chaisakulsurin Shariff Manuthasna Tanawish Masri Thanayuth Panyalert Kritsada Palee Pakawat Prasit Patcharin Kamsing Peerapong Torteeka Thai Space Consortium Project National Astronomical Research Institute of Thailand (Public Organization) Chiang-Mai Thailand Center of Observatory Operation and Engineering National Astronomical Research Institute of Thailand (Public Organization) Chiang-Mai Thailand Faculty of Engineering Program in Robotics and Computational Intelligence Systems International Academy of Aviation Industry King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Aeronautical Engineering Air-Space Control Optimization and Management Laboratory International Academy of Aviation Industry King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

This paper proposes a Hardware-in-the-Loop sim-ulation (HiLs) tested via square Helmholtz coils as a relative magnetic field generator. In technical terms, the HiLs is an indispensable tool for engineering design during rapid proto-typing of attitude determination and control algorithms for the turning parameters that control the attitude of the satellite, since most of the satellite's mission relies on its attitude, making this system one of the most essential to the satellite's operation. More-over, performing controlled experiments with the parameters for developing adaptive control algorithms improves the overall efficiency of the satellite's kinematic system. The conceptual design of a proposed system architecture can be composed of the electrical currents of 2.4-meter square Helmholtz coils produced by a low-level microcontroller equipped with a DC-motor driver by a pulse-width modulation (PWM) signal through real-time connection to an orbit propagator using a high-level computer. This research focuses on the attitude dynamic of satellites through the interaction between the Earth's magnetic field (EMF) and the magnetotorque in the satellite. To apply this phenomenon, the intensity and the direction of the magnetic field must be identified through Biot-Savart's law. Along with the EMF, the reference position is calculated using the standard general perturbations satellite orbit model (SGP4), and the intensity is modeled based on coefficients from the 13 th edition of the International Geo-magnetic Reference Field (IGRF). Therefore, this paper presents a detailed development of a HiLs testbed for distributed attitude determination and control systems (hardware and software co-design, protocol, and control theory). Furthermore, it discusses a classic cooperative control case for the output of magnetic field intensity and direction, which was undertaken to explain the integrated simulation process and validate the effectiveness of the co-simulation tested to be

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WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle Avoidance in Dynamic Unmapped Environments Using a Laser Scanning System (LiDAR)

WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle...

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IEEE Latin American robotics Symposium, LARS

作者： Jonathan Cerbaro Dieisson Martinelli André Schneider de Oliveira João Alberto Fabro ederal University of Technology - Paraná (UTFPR) Graduate Program on Electrical Engineering and Applied Informatics (CPGEI) Advanced Laboratory of Embedded Systems and Robotics (LASER) Curitiba Paraná Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Fuzzy logic is a control technique commonly applied in mobile robotics. This paper contains the analysis of three implementations of control systems for obstacle avoidance of a mobile robot. The simulation environment simulate situations with both fixed and dynamic obstacles - like people walking - aiming to compare the fuzzy control approach most suitable to avoid different kinds of obstacles. The analyzed approaches differ by segmenting the sensor data (provided by a LiDAR sensor) into three regions, five regions or by considering only the position of the nearest obstacle. The three approaches were tested in scenarios containing different obstacles and in a simulation where the robot delivers to tables in a dynamic environment, simulating a social event as an application example of a waiter robot.

关键词： Robot sensing systems Robots Collision avoidance Robot kinematics Laser radar Fuzzy logic Input variables

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Low-latency perception in off-road dynamical low visibility environments

arXiv

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arXiv 2020年

作者： Alves, Nelson Ruiz, Marco Reis, Marco Cajahyba, Tiago Oliveira, Davi Barreto, Ana Simas Filho, Eduardo F. de Oliveira, Wagner L.A. Schnitman, Leizer Monteiro, Roberto L.S. Electrical Engineering Program Federal University of Bahia Salvador Brazil Brazilian Institute of Robotics SENAI CIMATEC Salvador Brazil Federal University of Bahia PPGM Salvador Brazil Computational Modeling and Industrial Technology Program SENAI CIMATEC Salvador Brazil

This work proposes a perception system for autonomous vehicles and advanced driver assistance specialized on unpaved roads and off-road environments. In this research, the authors have investigated the behavior of Deep Learning algorithms applied to semantic segmentation of off-road environments and unpaved roads under differents adverse conditions of visibility. Almost 12,000 images of different unpaved and off-road environments were collected and labeled. It was assembled an off-road proving ground exclusively for its development. The proposed dataset also contains many adverse situations such as rain, dust, and low light. To develop the system, we have used convolutional neural networks trained to segment obstacles and areas where the car can pass through. We developed a Configurable Modular Segmentation Network (CMSNet) framework to help create different architectures arrangements and test them on the proposed dataset. Besides, we also have ported some CMSNet configurations by removing and fusing many layers using TensorRT, C++, and CUDA to achieve embedded real-time inference and allow field tests. The main contributions of this work are: a new dataset for unpaved roads and off-roads environments containing many adverse conditions such as night, rain, and dust;a CMSNet framework;an investigation regarding the feasibility of applying deep learning to detect region where the vehicle can pass through when there is no clear boundary of the track;a study of how our proposed segmentation algorithms behave in different severity levels of visibility impairment;and an evaluation of field tests carried out with semantic segmentation architectures ported for real-time inference. The proposed dataset (named Kamino) is available at https: // github. com/ Brazilian-Institute-of-robotics/ offroad_ dataset, and the experiments at https: // github. com/ Brazilian-Institute-of-robotics/ autonomous_ perception . © 2020, CC BY-NC-ND.

关键词： Dust

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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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