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20th Polish control Conference, PCC 2020

作者： Pazderski, Dariusz Institute of Automatic Control and Robotics Poznan University of Technology Poznań Poland

ISBN: (纸本)9783030509354

The paper deals with an alternative approach to control of a group of unicycle-like robots. The proposed algorithm is based on an approximate linearisation taking advantage of a dynamic feedback employing a transverse function. As a result, unicycle-like robots can be roughly perceived as unconstrained planar systems for which typical potential-like navigation methods can be used. In this paper both position and orientation stabilisation problem are discussed and a basic navigation algorithm for dynamic and static circular obstacles is proposed. The considered control/navigation method is verified numerically for the selected simulation scenario. © 2020, Springer Nature Switzerland AG.

关键词： Multi agent systems

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Lining-Up Stabilizers for Pusher and Puller Articulated Vehicles 20th

Lining-Up Stabilizers for Pusher and Puller Articulated Vehi...

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20th Polish control Conference, PCC 2020

作者： Michalek, Maciej Marcin Institute of Automatic Control and Robotics Poznan University of Technology Poznań Poland

ISBN: (纸本)9783030509354

The paper presents a derivation, a local stability analysis, and a numerical validation of control systems with lining-up feedback stabilizers for two kinds of kinematic structures widely used for articulated vehicles in a transportation practice: the tractor-driven vehicle pulling a trailer with a steerable axle, and the wagon-driven vehicle pushing a non-driven prime-mover. Simulation results reveal effectiveness of the stabilizers for various motion strategies of the vehicles (backward, forward, and oscillatory). The stabilizers can be used in the intelligent articulated buses and tractor-trailer vehicles in a fully automated mode or as an advanced driver assistance system. © 2020, Springer Nature Switzerland AG.

关键词： Kinematics

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The problem of residual compensation effect 17

The problem of residual compensation effect

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17th IMEKO TC 10 and EUROLAB Virtual Conference: Global Trends in Testing, Diagnostics and Inspection for 2030

作者： Bartyś, Michal Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

This paper discusses the problem of the residual compensation effect. The residual compensation effect referred to as the fault compensation effect, is an underrated issue of a model-based diagnostics. In part, this is justified due to the relatively low probability of such an effect. However, there is a belief that the inability to isolate faults in case of the residual compensation effect is the evident drawback of the model-based diagnostics. This paper shows that under some conditions, the problem of fault compensation could be overcome. In this connection, the necessary and sufficient conditions of fault compensation effect for tri-valued residuals were formulated. Both conditions are explained in the example of the diagnosing of a simple single buffer tank system in open and closed-loop arrangements. In this regard, it was shown the complete disutility of a bi-valued residual evaluation frequently used for fault isolation. In contrast, the advantages of a tri-valued residual evaluation were outlined. This paper also brings a series of practical conclusions allowing for a better understanding of the residual compensation effect. © Global Trends in Testing, Diagnostics and Inspection for *** rights reserved.

关键词： Bi-valued residuals Diagnostics of processes Fault compensation effect Fault distinguishability Fault isolation Simulation model Tri-valued residuals

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Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras

arXiv

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arXiv 2023年

作者： Jeziorek, Kamil Pinna, Andrea Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University of Krakow Poland Sorbonne Universite CNRS LIP6 ParisF-75005 France

Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high dynamic range, low latency, and resistance to image blur. One promising approach for analyzing event data is through graph convolutional networks (GCNs). However, current research in this domain primarily focuses on optimizing computational costs, neglecting the associated memory costs. In this paper, we consider both factors together in order to achieve satisfying results and relatively low model complexity. For this purpose, we performed a comparative analysis of different graph convolution operations, considering factors such as execution time, the number of trainable model parameters, data format requirements, and training outcomes. Our results show a 450-fold reduction in the number of parameters for the feature extraction module and a 4.5-fold reduction in the size of the data representation while maintaining a classification accuracy of 52.3%, which is 6.3% higher compared to the operation used in state-of-the-art approaches. To further evaluate performance, we implemented the object detection architecture and evaluated its performance on the N-Caltech101 dataset. The results showed an accuracy of 53.7% mAP@0.5 and reached an execution rate of 82 graphs per second. © 2023, CC BY.

关键词： Cameras

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Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras

TechRxiv

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TechRxiv 2023年

作者： Jeziorek, Kamil Pinna, Andrea Kryjak, Tomasz Embedded Vision Systems Group Department of Automatic Control and Robotics AGH University Krakow Poland Sorbonne Universite CNRS LIP6 ParisF-75005 France

关键词： Convolution

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High-Speed Trajectory Tracking control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA

arXiv

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arXiv 2023年

作者： Baczmanski, MacIej Wasala, Mateusz Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics Agh University of Krakow Al. Mickiewicza 30 Krakow30-059 Poland

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by both high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 Vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 watts, compared to the minimum of 55 watts for weaker CPUs, and the small size (119mm x 140mm x 36mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than 97% of mAP (mean average precision) for object detection and above 90% of mIoU (mean intersection over union) score for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Event-Based Supervisors for the Maintenance Process of a Printing Machine

Event-Based Supervisors for the Maintenance Process of a Pri...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Nikolaos D. Kouvakas Fotis N. Koumboulis Dimitrios G. Fragkoulis Stelios Rempelos Department of Digital Industry Technologies School of Science Robotics Automatic Control and Cyber-Physical Systems Laboratory National and Kapodistrian University of Athens Euboea Greece Core Department National and Kapodistrian University of Athens Euboea Greece

A set of event-based supervisors is developed to control the maintenance process of a printing machine. The Discrete Event System descriptions of the distinct tasks of the maintenance process are in the form of Finite State Machines. The desired behavior is expressed as four behavioral rules. The rules are translated to regular languages realized by appropriate supervisor automaton. The supervisor’s physical realizability is proved and the blocking avoidance of the controlled printing machine is satisfied.

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Continuous-Time Ultra-Wideband-Inertial Fusion

arXiv

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arXiv 2023年

作者： Li, Kailai Cao, Ziyu Hanebeck, Uwe D. The Division of Automatic Control Department of Electrical Engineering Linköping University Sweden The Intelligent Sensor-Actuator-Systems Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany

We introduce a novel framework of continuous-time ultra-wideband-inertial sensor fusion for online motion estimation. Quaternion-based cubic cumulative B-splines are exploited for parameterizing motion states continuously over time. Systematic derivations of analytic kinematic interpolations and spatial differentiations are further provided. Based thereon, a new sliding-window spline fitting scheme is established for asynchronous multi-sensor fusion and online calibration. We conduct a dedicated validation of the quaternion spline fitting method, and evaluate the proposed system, SFUISE (spline fusion-based ultra-wideband-inertial state estimation), in real-world scenarios using public data set and experiments. The proposed sensor fusion system is real-time capable and delivers superior performance over state-of-the-art discrete-time schemes. We release the source code and own experimental data at https://***/KIT-ISAS/SFUISE . Copyright © 2023, The Authors. All rights reserved.

关键词： Motion estimation

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection

Visual Tracking Nonlinear Model Predictive Control Method fo...

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International Conference on Advanced robotics (ICAR)

作者： Abdelhakim Amer Mohit Mehndiratta Jonas le Fevre Sejersen Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark GIM Robotics Espoo Finland Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes 1 1 https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced.

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