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AUVSI's Unmanned systems North America 2004 - Proceedings

作者： Sugisaka, Masanori Akizuki, Kageo Albus, James S. Finkelstein, Robert Department of Electrical and Electronic Engineering Oita Univesity 700 Oaza Dannoharu Oita 870-1192 Japan at Nagoya Moriyamak-ku Nagoya 463-0003 Japan Graduate School of Information Production and Systems Waseda University 2-7 Hibikino Kitakyushu Fukuoka 808-0135 Japan Intelligent Systems Division National Institute of Standard and Technology Stop 8230 100 Bureau Drive Gaithersburg MD 20899-8230 United States Robotic Technology Inc. 11424 Palatine Drive Potomac MD 20854-1451 United States

This paper describes the techniques for command and control of autonomous mobile vehicle robots using a mobile phone and a mobile computer via Internet. The autonomous unmanned ground vehicle robot(UGV) developed at Artificial Life and robotics Laboratory of The University of Oita is called "Tarou". We show the techniques for Tarou to perform the tasks as follows;(1) an efficient sensor fusion from many sensors(vision, audio, geographic, etc.), (2) synthesizing and understanding the data including on-line image obtained from sensors installed with Tarou, (3) formulate the next course of action. The techniques developed enable Tarou to obtain the powerful ability for autonomous sensing and dynamic mobility for locomotion and navigation. The experiments for sending command and control to Tarou's tasks were performed successfully by the public Internet. The techniques developed could dramatically increase the performance of the autonomous unmanned ground vehicle robots for a future robotic defense system need.

关键词： Remotely operated vehicles

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Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

Models of basal ganglia and cerebellum for sensorimotor inte...

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Conference on sensor fusion and decentralized control in robotic systems III

作者： Jabri, MA Huang, J Coenen, OJMD Sejnowski, TJ Univ Sydney Sch Elect & Informat Engn Comp Engn Lab Sydney NSW 2006 Australia

ISBN: (纸本)0819438618

This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world-coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell, populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate ransformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion st

关键词： Cerebellum Visualization sensors Basal ganglia Neurons Light emitting diodes Visual process modeling Cameras Thalamus Brain

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Enhancing hybrid manufacturing with AI-driven real-time adaptive process control: integrating machine learning models and robotic systems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年 1-9页

作者： Swathi, Baswaraju Polyakov, Sergei Vladimirovich Kandavalli, Sumanth Ratna. Singh, Dinesh Kumar Murthy, Mantripragada Yaswanth Bhanu Gopi, Adapa New Horizon Coll Engn Dept Comp Sci & Engn Data Sci Bengaluru India Plekhanov Russian Univ Econ Moscow Russia Leonov Moscow Reg Univ Technol Korolev Russia NYU MetroTech Ctr 6 Tandon Sch Engn Dept Mech Engn Brooklyn NY USA Dr Shakuntala Misra Natl Rehabil Univ Dept Informat Technol Lucknow Uttar Pradesh India Vasireddy Venkatadri Inst Technol Guntur 522508 AP India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Green Fields Vaddeswaram 522302 Andhra Pradesh India

The potential of additive methods to integrate with machine learning (ML) models in hybrid manufacturing is significant in developing closed-loop processes for intelligent and adaptive control. This study presents a framework to defect mitigation that leverages a hybrid deep convolutional neural network (CNN) architecture with laser powder bed fusion additive systems. It connects on-site optical vision and sensor streams to identify process abnormalities and report real-time remedial actions. The hybrid CNN automatically connects reported anomalies to the physical actuation where quality issues might occur in a closed-loop fashion. This enables self-learning and autonomous corner-cutting while offering a pathway to new applications in digital manufacturing, such as surface finish optimisation and shape adaptation within a single build. In a case study, we demonstrate that our framework enables inspection and control at a level never seen before, as demonstrated by its precision in product inspection with an F-score of 93.8% and its ability to adjust control at a frequency exceeding 10 Hz. This work not only enables new learning paradigms for the development of real-time control systems for hybrid manufacturing, but also accelerates the process of AI-driven intelligent manufacturing by equipping industrial production systems with intelligence, flexibility and resilience. Our technique for tight coupling of process signals and actuation is a significant technical breakthrough in this critical pathway.

关键词： Hybrid manufacturing Convolutional neural networks Real-time control Adaptive process control Machine learning Deep learning

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autonomous systems: sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

Autonomous Systems: Sensors, Processing, and Security for Gr...

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autonomous systems: sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

ISBN: (纸本)9781510661967

The proceedings contain 26 papers. The topics discussed include: object detection in synthetic aerial imagery using deep learning;multi-sensor fusion 3D object detection for autonomous driving;fabrication of optically transparent millimeter-wave micromesh structures;adding RF situational awareness to robotic simulation systems;fingerprint enrollment using differential sensor amplifier circuitry for cyber-physical systems;noise injection techniques in cryptographic keys to enhance security of autonomous systems;efficient searches of physical unclonable function seed spaces for response-based cryptography;post-quantum cryptographic key distribution for autonomous systems operating in contested areas;and secure- and privacy-preserving policies for cooperative control of multiple vehicle systems.

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Coordinated decentralized Search for a Lost Target in a Bayesian World

Coordinated Decentralized Search for a Lost Target in a Baye...

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2003 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Bourgault, Frédéric Furukawa, Tomonari Durrant-Whyte, Hugh F. Australian Centre for Field Robotics University of Sydney Sydney NSW 2006 Australia School of Mech. and Manuf. Eng. University of New South Wales Sydney NSW 2052 Australia

This paper describes a decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the target state PDF through a Bayesian DDF network enabling them to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real time adaptability can be achieved. The effectiveness of the approach is demonstrated in different scenarios by implementing the framework for a team of airborne search vehicles looking for a stationary, and a drifting target lost at sea.

关键词： sensor data fusion

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An experimental evaluation of bayesian soft human sensor fusion in robotic systems

An experimental evaluation of bayesian soft human sensor fus...

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AIAA Guidance, Navigation, and control Conference 2012

作者： Sample, Eric Ahmed, Nisar Campbell, Mark Dept. of Mechanical and Aerospace Engineering Cornell University Ithaca NY United States

ISBN: (数字)9781600869389

ISBN: (纸本)9781600869389

As we move forward into the twenty-first century, we are seeing new horizons opening to us through the use of autonomous robots as explorers, going places we can't or won't. The interaction between the human operator and robotic agent is of paramount importance in exploring these new horizons. Both humans and robots have their strengths and weak- nesses, and this paper explores how they compliment each other and looks at new, more eficient ways to facilitate communication between the robot and its operator. This paper investigates conditions to the use of Bayesian information fusion algorithms with Gaussian mixture models to fuse soft human input with robotic sensor data to complete cooperative mission objectives. The condition investigated here are the benefits of training individual models for the human input, or if a generic model trained on many people is suficient to complete the mission;we are also seeking to determine if it is useful to allow the human operator to assign a confidence value to the information sent to the robot. These two conditions are evaluated with sizable number of human subjects working with a robotic platform to complete a multi-target search mission. Along with the results of these two conditions, reactions to the limited interaction with a robotic partner of a variety of people will be presented based on a survey that was performed once the trials were completed. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Personnel training

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sensor fusion FOR CL0SED-LO0P control OF UPPER-LIMB PROSTHESES

SENSOR FUSION FOR CL0SED-LO0P CONTROL OF UPPER-LIMB PROSTHES...

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作者： Markovic Marko University of Göttingen

学位级别：博士

This thesis addresses open challenges in the field of myoelectrically controlled upper limb prostheses. These challenges include the inherently low bandwidth of the myoelectric control channel that makes the current control interfaces limited and unintuitive for the user, especially when controlling modern multi-function prostheses, as well as the lack of somatosensory feedback that would allow the user to better perceive the state of his/her prosthesis. This thesis aims at addressing these challenges by designing novel man-machine-interfaces, based on the latest sensing and automatic control technologies, to provide improved operation and perception of the prosthetic device. To this end, the thesis comprises introductory chapters that describe the state of the art in the field, the aim of the thesis and the used methodology, as well as four peer- reviewed journal publications presenting the novel feedforward and feedback methods. In the first two studies, I proposed and evaluated a novel system for prosthesis control based on sensor-fusion. In the classic approach, the user has the responsibility of generating all the command signals, while the prosthesis controller operates as a decoder, acquiring the signals and decoding user intention. In the novel framework, proposed here, the prosthesis is enhanced with advanced sensing and autonomous decision-making, becoming thereby an intelligent agent assisting the user. The inspiration for this novel approach comes from the modern-day autonomous robotic systems that utilize a variety of multimodal sensors and data processing methods in order to perceive and interpret the environment. In the present work, the prosthetic hand was equipped with computer vision and inertial sensing, and this information was used by the prosthesis controller to provide an additional, artificial-intelligence processing layer. This component analyzed the usage-context (environment, user and prosthesis) and, based on this, automatically controlle

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Gaussian Process Online Learning With a Sparse Data Stream

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IEEE roboticS AND AUTOMATION LETTERS 2020年第4期5卷 5977-5984页

作者： Park, Jehyun Choi, Jongeun Yonsei Univ Sch Mech Engn Seoul 03722 South Korea

Gaussian processes (GPs) have been exploited for various applications even including online learning. To learn time-varying hyperparameters from an information-limited sparse data stream, we consider the infinite-horizon Gaussian process (IHGP) with a low computational complexity. For example, the IHGP framework could provide efficient GP online learning with a sparse data stream in mobile devices. However, we show that the originally proposed IHGP has difficulty in learning time-varying hyperparameters online from the sparse data stream due to the numerically approximated gradient of the marginal likelihood function. In this paper, we show how to extend the IHGP in order to learn time-varying hyperparameters using a sparse and non-stationary data stream. In particular, our solution approach offers the exact gradient as the solution of a Lyapunov equation. Therefore, our approach achieves better performance with a sparse data stream while still keeping the computational complexity low. Finally, we present the comparison results to demonstrate that our approach outperforms the originally proposed IHGP on practical applications with sparse data streams. To demonstrate the effectiveness of our approach, we consider a multi-rate sensor fusion or an interpolation problem where slow vision systems need to be combined with other fast sensory units for feedback control in the field of autonomous driving. In particular, we apply our approach to vehicle lateral position error estimation together with a deep learning model for autonomous driving using non-stationary lateral position error signals in a model-free and data-driven fashion.

关键词： Computer vision for other robotic applications optimization and optimal control probability and statistical methods

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Multi sensor fusion based framework for efficient mobile robot collision avoidance and path following system

Multi sensor fusion based framework for efficient mobile rob...

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作者： Almasri, Marwah University of Bridgeport

学位级别：Ph.D.

The field of autonomous mobile robotics has recently gained the interests of many researchers. Due to the specific needs required by various applications of mobile robot systems (especially in navigation), designing a real-time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. Therefore, an efficient collision avoidance and path following methodology is needed to develop an intelligent and effective autonomous mobile robot system. Mobile robots are equipped with various types of sensors (such as GPS, camera, infrared and ultrasonic sensors); these sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. A new technique for line following and collision avoidance in the mobile robotic systems is introduced. The proposed technique relies on the use of infrared sensors and involves a reasonable level of calculations, to be easily used in real-time control applications. In addition, a fusion model based on fuzzy logic is proposed. Eight distance sensors and a range finder camera are used for the collision avoidance approach, where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs (which are the eight distance sensors and the camera), two outputs (which are the left and right velocities of the mobile robot’s wheels), and twenty four fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and robot to show the ability of the robot to follow a path, detect obstacles, and navigate around them to avoid collision. It also shows that the robot has been successfully following extremely congested curves and has avoided any obstacle that emerged on its path. The proposed methodology which includes the collision avoidance based on

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Designing autonomous Mobile Robots 1

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2004年

作者： Holland John M.

ISBN: (数字)9780080477183

ISBN: (纸本)9780750676830

Designing autonomous Mobile Robots introduces the reader to the fundamental concepts of this complex field. The author addresses all the pertinent topics of the electronic hardware and software of mobile robot design, with particular emphasis on the more difficult problems of control, navigation, and sensor interfacing. Covering topics such as advanced sensor fusion, control systems for a wide array of application sensors and instrumentation, and fuzzy logic applications, this volume is essential reading for engineers undertaking robotics projects as well as undergraduate and graduate students studying robotic engineering, artificial intelligence, and cognitive science. Its state-of-the-art treatment of core concepts in mobile robotics helps and challenges readers in exploring new avenues in an exciting field. Authored by a well-known pioneer of mobile robotics Learn how to approach the design of and complex control system with confidence

关键词：一般工业技术自动化技术、计算机技术

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