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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Hashemi, Seyed Hamed Mattila, Jouni Tampere University Unit of Automation Technology and Mechanical Engineering Faculty of Engineering and Natural Sciences Tampere Finland

ISBN: (纸本)9798350373578

This paper peruses the problem of controlling robotic manipulators by utilizing visual feedback signals while meeting safety requirements and ensuring closed-loop stability. Accordingly, the paper employs a hybrid observer to estimate an end-effector pose (position and orientation) that combines tip-frame acceleration and angular velocity measurements obtained from an inertial measurement unit with bearing measurements of known landmarks obtained from a stereo camera. The estimated pose is then utilized as a Cartesian feedback to close the control loop. A virtual decomposition control (VDC) method is introduced to control the manipulator, as it is well-suited for controlling complex robots like exoskeletons and humanoid robots. For the first time, by utilizing a quadratic program, control barrier functions are incorporated with VDC as a safety filter, enabling the robot manipulator to avoid potential collisions involving both humans and external obstacles and to handle joint limits. By means of the Lyapunov stability theorem, the combined observer-controller scheme is guaranteed to be asymptotically stable. To demonstrate the effectiveness of the proposed vision-based control structure, a simulation study is performed on a 6-degrees-of-freedom (DoF) long-reach manipulator. © 2024 IEEE.

关键词： Quadratic programming

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Instructing Hierarchical Tasks to Robots by Verbal Commands

Instructing Hierarchical Tasks to Robots by Verbal Commands

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Telkes, Peter Angleraud, Alexandre Pieters, Roel Tampere University Cognitive Robotics Group Unit of Automation Technology and Mechanical Engineering Tampere33720 Finland

ISBN: (纸本)9798350312072

Natural language is an effective tool for communication, as information can be expressed in different ways and at different levels of complexity. Verbal commands, utilized for instructing robot tasks, can therefor replace traditional robot programming techniques, and provide a more expressive means to assign actions and enable collaboration. However, the challenge of utilizing speech for robot programming is how actions and targets can be grounded to physical entities in the world. In addition, to be time-efficient, a balance needs to be found between fine- and course-grained commands and natural language phrases. In this work we provide a framework for instructing tasks to robots by verbal commands. The framework includes functionalities for single commands to actions and targets, as well as longer-term sequences of actions, thereby providing a hierarchical structure to the robot tasks. Experimental evaluation demonstrates the functionalities of the framework by human collaboration with a robot in different tasks, with different levels of complexity. The tools are provided open-source at https://***/voice-jogger/ © 2024 IEEE.

关键词： Robot programming

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Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

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Chinese Physics B 2023年第2期32卷 53-59页

作者：程鑫鲁秀娟刘亚楠匡森 Department of Automation University of Science and Technology of ChinaHefei 230027China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China Quantum Machines Unit Okinawa Institute of Science and Technology Graduate UniversityOkinawa 904-0495Japan

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria *** conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

关键词： quantum control state preparation intelligent optimization algorithm

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Air tightness detection method in parallel for aerospace: mechanical design, system modeling and optimum control 2

Air tightness detection method in parallel for aerospace: Me...

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2023 2nd International Conference on Aerospace and Control engineering, ICoACE 2023

作者： Yang, Bo Yuan, Jianhui Wang, Xiaoliang Zhang, Jianwei Wu, Wei Xie, Mingyang Unit 96815 PLA Jingdezhen333000 China School of Mechanical Science & Engineering Huazhong University of Science and Technology Wuhan430074 China College of Automation Nanjing University of Aeronautics and Astronautics Nanjing210016 China

To address the problems of high cost, complex operation, and low efficiency of existing equipment for aerospace, a simple but efficient multichannel air tightness detection device is designed. A physical system model is constructed for the process of inflation, deflation, and inflation with leakage. The expression between the inflation/deflation rate and the valve area is obtained. Through modeling the intersection and rotation of spatial curves, the relationship between the valve area and the valve core angle is derived, and the optimal valve core angle calculation method is proposed to achieve precise rate control of inflation and deflation. Simulation experiments and numerical analysis demonstrate the superiority of the proposed system scheme, the effectiveness of the control method, as well as the accuracy of the mathematical model. For the tested object of 0.3 L, the target pressure of 400 KPa, and the inflation and deflation rate of 75 Kpa/min, by using four channels with a 10 s interval for sequential operation and adjusting valves every 60 s, the efficiency of inflation and deflation is more than 3.5 times that of a single device with a single circuit. Its performance is not much different from real-time automatic control equipment, but the economy is greatly improved. © Published under licence by IOP Publishing Ltd.

关键词： Efficiency

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Safety-guaranteed Virtual Decomposition-based Control of Robot Manipulators using Visual Feedback

Safety-guaranteed Virtual Decomposition-based Control of Rob...

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IEEE-RAS International Conference on Humanoid Robots

作者： Seyed Hamed Hashemi Jouni Mattila Unit of Automation Technology and Mechanical Engineering Faculty of Engineering and Natural Sciences Tampere University Tampere Finland

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

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Instructing Hierarchical Tasks to Robots by Verbal Commands

Instructing Hierarchical Tasks to Robots by Verbal Commands

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IEEE/SICE International Symposium on System Integration

作者： Péter Telkes Alexandre Angleraud Roel Pieters Cognitive Robotics group Unit of Automation Technology and Mechanical Engineering Tampere University Tampere Finland

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Automatic Robot Path Planning for Active Visual Inspection on Free-Form Surfaces

Automatic Robot Path Planning for Active Visual Inspection o...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Osama Tasneem Roel Pieters Cognitive Robotics Group Unit of Automation Technology and Mechanical Engineering Tampere University Tampere Finland

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined inspection poses and training images essential for the training of models. The acquisition of such data and their integration into an inspection framework is challenging due to the variety in objects and scenes involved and due to additional bottlenecks caused by the manual collection of training data by humans, thereby hindering the automation of visual inspection across diverse domains. This work proposes a solution for automatic path planning using a single depth camera mounted on a robot manipulator. Point clouds obtained from the depth images are processed and filtered to extract object profiles and transformed to inspection target paths for the robot end-effector. The approach relies on the geometry of the object and generates an inspection path that follows the shape normal to the surface. Depending on the object size and shape, inspection paths can be defined as single or multi-path plans. Results are demonstrated in both simulated and real-world environments, yielding promising inspection paths for objects with varying sizes and shapes. Code and video are open-source available at: https://***/CuriousLad1000/Auto-Path-Planner

关键词： Point cloud compression Training Visualization Shape Robot vision systems Training data Inspection Cameras Path planning Hardware

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Multi-label Annotation for Visual Multi-Task Learning Models

arXiv

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arXiv 2023年

作者： Sharma, Gaurang Angleraud, Alexandre Pieters, Roel Unit of Automation Technology and Mechanical Engineering Tampere University Tampere Finland

Deep learning requires large amounts of data, and a well-defined pipeline for labeling and augmentation. Current solutions support numerous computer vision tasks with dedicated annotation types and formats, such as bounding boxes, polygons, and key points. These annotations can be combined into a single data format to benefit approaches such as multi-task models. However, to our knowledge, no available labeling tool supports the export functionality for a combined benchmark format, and no augmentation library supports transformations for the combination of all. In this work, these functionalities are presented, with visual data annotation and augmentation to train a multi-task model (object detection, segmentation, and key point extraction). The tools are demonstrated in two robot perception use cases. © 2023, CC BY.

关键词： Object detection

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A Normal Behavior Model Based on Machine Learning for Wind Turbine Cyber-Attack Detection

A Normal Behavior Model Based on Machine Learning for Wind T...

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Artificial Intelligence and Machine Learning for Energy Transformation (AIE), International Workshop on

作者： Hao Wu Hamed Badihi Yali Xue Matti Vilkko College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Automation Technology and Mechanical Engineering Unit Faculty of Engineering and Natural Sciences Tampere University Tampere Finland

ISBN: (数字)9798350364965

ISBN: (纸本)9798350364972

As the global power landscape increasingly incorporates wind energy, wind turbine infrastructure has become a target for sophisticated cyber-attacks. These cyber-attacks, ingeniously crafted to infiltrate the cyber layer of wind turbine cyber-physical systems, can significantly impair system performance and potentially lead to severe cascading damages, aligned with the attackers' nefarious objectives. The stealthy nature of these sophisticated cyber-attacks renders their anomalous behaviors and patterns more challenging to identify than conventional faults, highlighting the urgent need for novel, specialized anomaly detection strategies tailored to wind turbine cyber-attacks. Addressing this critical concern, this paper proposes a machine learning-based normal behavior modeling approach designed to effectively detect anomalies induced by a new coordinated type of stealthy cyber-attack on wind turbines. This is achieved through advanced analysis and processing of the system's measured data, along with precise residual generation and evaluation. The efficiency of the proposed approach is demonstrated using an offshore wind turbine benchmark, factoring in wind turbulence, measurement noise, and complex cyber-attack scenarios.

关键词： Fault diagnosis Wind energy System performance Conferences Noise Machine learning Cyber-physical systems

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Sensor-Based Human-Robot Collaboration for Industrial Tasks

SSRN

引用

SSRN 2023年

作者： Angleraud, Alexandre Ekrekli, Akif Samarawickrama, Kulunu Sharma, Gaurang Pieters, Roel Unit of Automation Technology and Mechanical Engineering Tampere University Korkeakoulunkatu 6 Tampere Finland

Collaboration between human and robot requires interaction modalities that suit the context of the shared tasks and the environment in which it takes place. While an industrial environment can be tailored to favor certain conditions (e.g., lighting), some limitations cannot so easily be addressed (e.g., noise, dirt). In addition, operators are typically continuously active and cannot spare long time instances away from their tasks engaging with physical user interfaces. Sensor-based approaches that recognize humans and their actions to interact with a robot have therefor great potential. This work demonstrates how human-robot collaboration can be supported by visual perception models, for the detection of objects, targets, humans and their actions. For each model we present details with respect to the required data, the training of a model and its inference on real images. Moreover, we provide all developments for the integration of the models to an industrially relevant use case, in terms of software for training data generation and human-robot collaboration experiments. These are available open-source in the OpenDR toolkit at https://***/opendr-eu/opendr. Results are discussed in terms of performance and robustness of the models, and their limitations. Although the results are promising, learning-based models are not trivial to apply to new situations or tasks. Therefore, we discuss the challenges identified, when integrating them into an industrially relevant environment. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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