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24th International Conference on Computer Applications in Industry and Engineering, CAINE 2011

作者： Paolini, Christopher Huber, Gerold Collier, Quentin Lee, Gordon K. Dept of Electrical and Com Engr San Diego State University 5500 Campanile Drive San Diego CA 92182 United States Management Center Innsbruck University of Applied Sciences Universitatsstrae 15 6020 Innsbruck Austria IUT de Bethune Networks and Telecomm Dept 62408 Bethune France

ISBN: (纸本)9781880843833

This paper presents a hardware system architecture for a mobile robotic system that employs multiple sensors (an ultrasonic array, a thermal sensor, and a video streaming system) to obtain information about the environment, a virtual field strategy for obstacle avoidance and path planning, and an ANFIS controller for path tracking. The particular focus is on the ANFIS controller and its hardware implementation, the graphics-user interface and the overall system architecture. Experimental results and preliminary evaluation show that the proposed architecture is a feasible one for autonomous mobile robotic systems.

关键词： controllers

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Energy-Efficient Industrial Internet of Things in Green 6G Networks

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8558页

作者： Fernando, Xavier Lazaroiu, George Toronto Metropolitan Univ Intelligent Commun & Comp Lab Toronto ON M5B 2K3 Canada Curtin Univ Fac Sci & Engn Bentley WA 6102 Australia Spiru Haret Univ Dept Econ Sci Bucharest 030045 Romania

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

关键词： energy Industrial Internet of Things green 6G network vehicle sensor

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Force, acceleration and vision sensor fusion for contour following tasks with an industrial robot

Force, acceleration and vision sensor fusion for contour fol...

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2011 9th IEEE International Symposium on robotic and sensors Environments, ROSE 2011

作者： Koch, Heiko König, Alexander Weigl-Seitz, Alexandra Kleinmann, Karl Suchý, Jozef University of Applied Sciences Darmstadt Birkenweg 8 64295 Darmstadt Germany Chemnitz Technical University Reichenhainer Str. 70 D-09126 Chemnitz Germany

ISBN: (纸本)9781457708190

In robotic contour following tasks such as sewing or cutting an industrial robot guides a tool along a contour of a workpiece. Teaching the task manually is very time consuming and the process would be unable to react to uncertainties or changes of the environment. Thus sensor systems are applied to reduce human intervention, making the robot more autonomous. In this paper we present an integrated approach for multisensor contour following. A look-ahead vision sensor guides the tool along a contour of a workpiece while force feedback control maintains a desired contact force. Couplings that arise between these two controllers are estimated and compensated through online measurements of contact forces between robot and environment. The identification of the environment combines force and acceleration sensors in an observer-based control scheme. The system is validated by experiments that involve contour following on compliant objects © 2011 IEEE.

关键词： Industrial robots

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Atlas fusion - Modern Framework for autonomous Agent sensor Data fusion 14

Atlas Fusion - Modern Framework for Autonomous Agent Sensor ...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Ligocki, Adam Jelinek, Ales Zalud, Ludek Ceitec Brno University of Technology Cybernetics and Robotics Brno Czech Republic

ISBN: (纸本)9781665467261

In this paper, we present our software sensor fusion framework for self-driving cars and other autonomous robots. We have designed our framework as a universal and scalable platform for building up a robust 3D model of the agent's surrounding environment by fusing a wide range of various sensors into the data model that we can use as a basement for the decision making and planning algorithms. Our software currently covers the data fusion of the RGB and thermal cameras, 3D LiDARs, 3D IMU, and a GNSS positioning. The framework covers a complete pipeline from data loading, filtering, preprocessing, environment model construction, visualization, and data storage. The architecture allows the community to modify the existing setup or to extend our solution with new ideas. The entire software is fully compatible with ROS (robotic Operation System), which allows the framework to cooperate with other ROS-based software. The source codes are fully available as an open-source under the MIT license. See https://***/robotics-BUT/Atlas-fusion. © 2022 IEEE.

关键词： autonomous vehicles

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autonomous Navigation and Mapping in Underground Mines Using Aerial Robots

Autonomous Navigation and Mapping in Underground Mines Using...

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IEEE Aerospace Conference

作者： Papachristos, Christos Khattak, Shehryar Mascarich, Frank Alexis, Kostas Univ Nevada Autonomous Robots Lab Reno NV 89557 USA

ISBN: (纸本)9781538668542

In this work we present an integrated approach for autonomous navigation and mapping in underground mines using aerial robots. Underground mines present a set of critical challenges as they are not only GPS-denied by nature, but their environmental circumstances also lead to severe sensor degradation ( due to combinations of darkness, dust, and smoke), localizability problems ( due to textureless surfaces and locally self-similar structure), while also presenting stringent navigation conditions as a result of certain very narrow geometries. To address these issues, the presented robotic systems integrate a multi-modal sensing suite and a set of associated fusion algorithms that enable simultaneous localization and mapping in the GPS-denied and visually-degraded environment of underground mines. More specifically, the fusion of visual and thermal cameras, LiDAR, as well as Inertial Measurement Unit cues is investigated in order to provide informative data in textureless, dark, and obscurants-filled settings. Provided the capacity for reliable pose estimation and mapping the surroundings despite the sensing degradation, autonomous navigation then becomes possible. Model Predictive control is used to enable precise trajectory tracking and robustness against disturbances. Path planning for collision-avoidance and autonomous exploration is facilitated through a receding horizon sampling-based algorithm that further accounts for the vehicle dynamic constraints and its limited endurance. The integrated system is tested in field experiments inside underground metal mines in Northern Nevada. The presented results correspond to the navigation and mapping of mine drifts and headings and involve environments that are completely dark, dust-filled, and particularly textureless. As shown, the proposed approach ensures the robust and reliable autonomous navigation of aerial robots in underground mines, alongside consistent mapping.

关键词： Robot sensing systems autonomous robots Cameras Unmanned aerial vehicles Degradation

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I(CES)-Cubes: A modular self-reconfigurable bipartite robotic system

I(CES)-Cubes: A modular self-reconfigurable bipartite roboti...

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Proceedings of the 1999 sensor fusion and decentralized control in robotic systems II

作者： Unsal, Cem Kiliccote, Hah Khosla, Pradeep K. Carnegie Mellon Univ Pittsburgh United States

In this manuscript, we introduce I(CES)-Cubes, a class of three-dimensional modular robotic system that is capable of reconfiguring itself in order to adapt to its environment. This is a bipartite system, i.e., a collection of (i) active elements capable of actuation, and (ii) passive elements acting as connectors between actuated elements. Active elements, called links, are 3-DOF manipulators that are capable of attaching/detaching themselves to/from the passive elements (cubes). The cubes can then be positioned and oriented using links, which are independent mechatronic elements. Self-reconfiguration property enables the system to perform locomotion tasks over difficult terrain. For example, the system would be capable of moving over obstacles and climbing stairs. These tasks are performed by positioning and orienting cubes and links to form a three-dimensional network with required shape and position. This paper describes the design of the passive and active elements, the attachment mechanisms, and several reconfiguration scenarios. Specifics of the hardware implementation and results of experiments with current prototypes are also given.

关键词： Modular robots

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Incremental Light Bundle Adjustment for robotics Navigation

Incremental Light Bundle Adjustment for Robotics Navigation

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Indelman, Vadim Melim, Andrew Dellaert, Frank Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

ISBN: (纸本)9781467363587

This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points. Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion [10], to information fusion in robotics navigation and in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain high-rate performance over time, consecutive IMU measurements are summarized using a recently-developed technique and navigation states are added to the optimization only at camera rate. If required, the observed 3D points can be reconstructed at any time based on the optimized robot's poses. The proposed method is compared to BA both in terms of accuracy and computational complexity in a statistical simulation study.

关键词： computational complexity control engineering computing inference mechanisms mobile robots navigation optimisation robot vision sensor fusion

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LiDAR-Aided Relative and Absolute Localization for Automated UAV-based Inspection of Aircraft Fuselages

LiDAR-Aided Relative and Absolute Localization for Automated...

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IEEE International Conference on Multisensor fusion and Integration for Intelligent systems (MFI)

作者： Pugliese, Roland Konrad, Thomas Abel, Dirk Rhein Westfal TH Aachen Inst Automat Control D-52074 Aachen Germany

ISBN: (纸本)9781665445214

Robust localization is a crucial task for various robotic applications, such as the inspection of aircraft surfaces by autonomous Unmanned Aerial Vehicles (UAV). Currently, aircraft inspection in mostly done in GNSS-denied hangar environments but UAV indoor localization using external positioning systems has several deficits regarding practicability, safety and security. Since aircraft standing times are the crucial cost factor, inspection should perspectively be shifted outdoors to the runway in order to further increase efficiency, which requires an onboard localization of the UAV relative to the aircraft that ensures robustness by working independendly of hangar features or external hardware. This paper proposes a novel approach to exploit a-priori knowledge of a cylindrical shaped inspection object, such as an aircraft fuselage, in order to support and supplement the position state estimation of the UAV. The relative localization approach comprises shape fitting of a cylinder model into LiDAR measurements of the aircraft using Random Sample Consensus (RANSAC), preceded by an intensity-based prefiltering, resulting in a significant increase of performance. This method can be applied to individual LiDAR sweeps, enabling onboard computation in soft real-time with a cycle time of 100 ms. Moreover, with the help of the onboard LiDAR-derived relative positioning to the aircraft, the problem of continuous line of sight to any transmitter of an indoor navigation system (iGPS) is alleviated by supplementing an existing sensor fusion for absolute localization, while maintaining an accuracy requirement in the submeter range. The feasibility of the proposed approach is verified experimentally by evaluating measurements obtained in a hangar with a Boeing 737-500.

关键词： Location awareness Laser radar Shape Shape measurement Inspection sensor fusion Unmanned aerial vehicles

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A flexible framework for mobile robot pose estimation and multi-sensor self-calibration

A flexible framework for mobile robot pose estimation and mu...

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10th International Conference on Informatics in control, Automation and robotics, ICINCO 2013

作者： Cucci, Davide Antonio Matteucci, Matteo Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy

ISBN: (纸本)9789898565716

The design and the development of the position and orientation tracking system of a mobile robot, and the calibration of its sensor parameters (e.g., displacement, misalignment and iron distortions), are challenging and time consuming tasks in every autonomous robotics project. The ROAMFREE framework delivers turn-on-and-go multi-sensors pose tracking and self-calibration modules and it is designed to be flexible and to adapt to every kind of mobile robotic platform. In ROAMFREE, the sensor data fusion problem is formulated as a hyper-graph optimization where nodes represent poses and calibration parameters and edges the non-linear measurement constraints. This formulation allows us to solve both the on-line pose tracking and the off-line sensor self-calibration problems. In this paper, we introduce the approach and we discuss a real platform case study, along with experimental results.

关键词： Mobile robots

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Human Grasp Intent Inference and Multimodal control in Prosthetic Hands

Human Grasp Intent Inference and Multimodal Control in Prost...

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作者： Han, Mo Northeastern University

学位级别：Ph.D., Doctor of Philosophy

Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal data may be collected from various environment sensors such as camera providing visual information, as well as easily-accessed human physiologic sensors including electromyographic (EMG) sensors. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control. As part of a multi-disciplinary project, i.e. HANDS project, which aims at designing a robotic hand as an upper limb prosthetic device, we developed two independent prosthetic control systems (HANDS V1 and HANDS V2) integrating multimodal sources of EMG and visual evidences into the control loop. Multiple grasps required for activities of daily living can be performed by both robotic systems which were developed in a lighter and cheaper semi-autonomous manner. The HANDS V1 system was first developed to provide an easy and convenient prosthesis with a portable EMG armband and a built-in palm camera, and hereafter the HANDS V2 was constructed as an upgraded solution of HANDS V1 to achieve more difficult tasks with more identified grasp types, more EMG channels and more complicated visual information involved. Both systems depend on multimodal signals from EMG and vision, where the EMG could reflect the physiologic features related to user intents, while the robustness and adaptability to different users could be retained by the visual information relying more on surrounding environments.

关键词： Human grasp Multimodal control Prosthetic hands

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