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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

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Evaluating the Impact of One-Shot Structured Pruning: A Noise Robustness Perspective

Evaluating the Impact of One-Shot Structured Pruning: A Nois...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Dieter Balemans Jens De Hoog thomas Huybrechts Jan Steckel Siegfried Mercelis IDLab - Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium CoSysLab - Faculty of Applied Engineering University of Antwerp Antwerp Belgium Flanders Make Strategic Research Centre Lommel Belgium

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

the increasing complexity of neural networks in combination with the growing interest in edge computing has led to a surge in the development of neural network compression techniques. these methods aim to reduce the computational complexity of the networks while maintaining a high level of performance. However, the impact of compression on the robustness of the network is often overlooked. therefore, in this work, we investigate the robustness of compressed neural networks by assessing the way a neural network behaves under different types and levels of noise. More specifically, we propose a robustness evaluation method that employs two different evaluation methodologies: a Multi-Level Monte Carlo simulation [1] and the CIFAR-100-C [2] dataset. We compare popular neural networks with their compressed versions, generated using an XAI-inspired one-shot structured channel pruning methodology. We systematically evaluate the robustness of the models pruned for different scenarios. Our results show that the pruned models can maintain their robustness properties in the presence of noise, and that specialization through pruning can improve a model's ability to handle corruptions.

关键词： Monte Carlo methods automation Noise Neural network compression Robustness Noise robustness Surges Computational complexity Robots Edge computing

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A Predictive Model for Morphogenesis of a Robot Sequence Mimicking Protein Folding

A Predictive Model for Morphogenesis of a Robot Sequence Mim...

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IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Zihao Ni Zhi Han Shixian Wang Yuping Gu Yongliang Yang School of Artificial Intelligence Shenyang University of Technology Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

the swarm phenomena observed in nature have inspired many control algorithms for diverse robot swarm systems. However, most of these endeavors are qualitative simulations aimed at understanding the mechanisms underlying biological swarm behavior. Reliably predicting morphologies of robot swarms from their features remains a formidable challenge. In molecular biology, the 3D structure of a protein molecule, which is a main factor of its functions, is mainly determined by its sequence of amino acids. Inspired by this, our lab developed RoboFold, a robot swarm platform for developing protein-folding-inspired methods controlling the morphologies of robot swarms. Using the data collected from this platform, we proposed a deep learning approach to predict the morphologies of swarm robotics based on their initial sequences. the goal of this research is predicting the initial sequence of robots forming the desired morphology. Preliminary results presented here demonstrated the efficacy of this method and underscore the feasibility of applying protein-folding-inspired methods to program robot swarms.

关键词： Proteins three-dimensional displays Morphology Swarm robotics Predictive models Self-organizing networks Programming Prediction algorithms Reliability Robots

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Using Laplace Transform to Optimize the Hallucination of Generation models

Using Laplace Transform to Optimize the Hallucination of Gen...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Cheng Kang Xinye Chen Daniel Novak Xujing Yao Department of Cybernetics Czech Technical University in Prague Prague Czech Republic Department of Numerical Mathematics Charles University Prague Czech Republic College of Computer and Information Engineering Nanjing Tech University Nanjing Jiangsu China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

To explore the feasibility of avoiding the confident error (or hallucination) of generation models (GMs), we for-malise the system of GMs as a class of stochastic dynamical systems through the lens of control theory. Numerous factors can be attributed to the hallucination of the learning process of GMs, utilising knowledge of control theory allows us to analyze their system functions and system responses. Due to the high complexity of GMs when using various optimization methods, we cannot figure out their solution of Laplace transform, but from a macroscopic perspective, simulating the source response provides a virtual way to address the hallucination of GMs. We also find that the training progress is consistent with the corresponding system response, which offers us a useful way to develop a better optimization component. Finally, the hallucination problem of GMs is fundamentally optimized by using Laplace transform analysis.

关键词： Training Learning systems Laplace equations Optimization methods Stochastic processes Process control Control theory Dynamical systems Robots Lenses

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Zero-shot Object Navigation with Vision-Language Foundation models Reasoning

Zero-shot Object Navigation with Vision-Language Foundation ...

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international conference on Autonomous Robots and Agents, ICARA

作者： Shuaihang Yuan Muhammad Shafique Mohamed Riyadh Baghdadi Farshad Khorrami Anthony Tzes Yi Fang NYUAD Center for Artificial Intelligence and Robotics (CAIR) Abu Dhabi UAE New York University Abu Dhabi UAE New York University US

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

this research introduces a novel method for zero-shot object navigation, enabling agents to navigate unexplored environments. Our approach differs from traditional methods, which often fail in new settings due to their dependence on large navigation datasets for training. We use Large Vision Language models (LVLMs) to help agents understand and move through unfamiliar visual environments without prior experience. the process involves using a pretrained LVLM for object detection to create a semantic map, followed by employing LVLM again to predict the likely location of the target object. Our experiments on the RobothOR benchmark show improved performance, with a 1.8% increase in both Success Rate and Success Weighted by Path Length (SPL) compared to the existing best method, ESC.

关键词： Training Visualization automation Navigation Semantics Object detection Benchmark testing

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Detection-segmentation convolutional neural network for autonomous vehicle perception

Detection-segmentation convolutional neural network for auto...

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international conference on methods and models in automation and robotics (MMAR)

作者： Maciej Baczmanski Robert Synoczek Mateusz Wasala Tomasz Kryjak Department of Automatic Control and Robotics Embedded Vision Systems Group AGH University of Krakow Poland

Object detection and segmentation are two core modules of an autonomous vehicle perception system. they should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor – the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP 50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. this architecture is a good solution for embedded perception systems for autonomous vehicles. the code is available at: https://***/vision-agh/MMAR_2023.

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Trajectory State Model-based Reinforcement Learning for Truck-trailer Reverse Driving

Trajectory State Model-based Reinforcement Learning for Truc...

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robotics, Control and automation (ICRCA), international conference on

作者： Hao Yan Mohamed A. Zohdy Edrees Alhawsawi Amr Mahmoud Electrical and Computer Engineering Department Oakland University Rochster USA

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Maneuver the truck-trailer wheeled robot (TTWR) system for reverse trajectory following is a challenging task even for experienced drivers, which requires advanced control strategies to ensure system stability, steering precision, and driving safety. In this paper, we present the integration of a small size network model with trajectory planning and reference state prediction to achieve remarkable sample efficiency compared with pure model-free reinforcement approaches. By employing the trajectory state prediction model as the foundation model to interact with the Proximal Policy Optimization (PPO) framework, we enable reversible access to the Markov Decision Process (MDP) dynamics and help the PPO to better estimate vehicle dynamics used for controller. A numerical simulation is conducted to demonstrate the trajectory following accuracy of the proposed methodology compared with popular industry control methods such as linear-quadratic regulator (LQR). Our results indicate that the proposed hybrid trajectory model based approach not only reduces the need for extensive data collection but also achieves similar control accuracy, suggesting a promising direction for future research in autonomous vehicle control, emphasizing the need for efficient, adaptable, and robust learning algorithms.

关键词： Adaptation models Accuracy Trajectory planning Predictive models Stability analysis Trajectory Numerical models

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Numerical Method for Stability Testing of Fractional Exponential Delay Systems

Numerical Method for Stability Testing of Fractional Exponen...

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international conference on methods and models in automation and robotics (MMAR)

作者： Tomasz P. Stefański Sebastian Dziedziewicz Piotr Kowalczyk Jacek Gulgowski Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Faculty of Mathematics Physics and Informatics University of Gdansk Gdansk Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

A numerical method for stability testing of fractional exponential systems including delays is presented in this contribution. We propose the numerical test of stability for a very general class of systems with a transfer function, which includes polynomials and exponentials of fractional powers of the Laplace variable s combined with delay terms. Such a system is unstable if any root of its characteristic equation, which usually involves transcendental functions, is localized on the right half-plane of the s-domain. Due to the infinite size of the right half-plane, the bilinear transformation is employed to map it onto the unit disc on the complex plane. then, the global roots and poles finding algorithm based on phase analysis is executed on the unit disc. that is, the roots of the characteristic equation for the considered system are detected with the use of an efficient algorithm based on domain meshing and Cauchy’s argument principle. In order to demonstrate the efficiency and applicability of the proposed numerical method, we executed stability tests for fractional exponential delay systems, which are considered benchmarking cases in other publications. It occurs that, each time, the proposed method correctly evaluates the system stability. However, unlike other methods, it is a very general technique that allows evaluation of almost any system, which does not require any preprocessing of the characteristic equation to execute the stability test.

关键词： Delay systems Force Transfer functions Benchmark testing Stability analysis Mathematical models Polynomials

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methods for Gastrointestinal Examination and Motion Simulation of a Roundworm-like Robot for Gastrointestinal Inspection

Methods for Gastrointestinal Examination and Motion Simulati...

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international conference on robotics and automation Engineering (ICRAE)

作者： Chang Xu Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

this article investigates the application of robotic systems, particularly a novel roundworm-like robot for gastrointestinal examinations. Unlike traditional endoscopic methods, this biomimetic approach leverages the movement patterns of roundworm-like to develop a non-invasive, efficient robotic system for navigating the human gastrointestinal tract. the proposed robot aims to enhance diagnostic accuracy and patient comfort by minimizing physical trauma and eliminating the need for anesthesia. this article utilizes a robotic model consisting of six rods and five joints to simulate the movement of a roundworm. the roundworm-like robot performs linear motion within a simulated model of the small intestine. By altering the length of each segment, the study aims to determine an appropriate size for the roundworm robot and analyze its feasibility. Initial simulations demonstrate the robot’s potential to adapt its movement through the simple environment of the gastrointestinal tract, offering a promising alternative to current diagnostic techniques.

关键词： Adaptation models Biological system modeling Heuristic algorithms Biomimetics Prototypes Robot sensing systems Gastrointestinal tract Stability analysis Real-time systems Reliability

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Study on the Psychological Acceptance of Level 3 Autonomous Driving

Study on the Psychological Acceptance of Level 3 Autonomous ...

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international conference on Control, automation and robotics (ICCAR)

作者： Yilian Li Wenyu Wu Chang Gao Chenhao Li School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

the emergence of autonomous vehicles (AVs) has brought numerous conveniences and development opportunities to human society, but it has also raised safety concerns. In recent years, AVs have been involved in multiple traffic accidents for various reasons, resulting in a continuous decline in user acceptance. In response, we need to propose some hypotheses to investigate the relationship between traffic accidents and the decline in user acceptance. this paper aims to summarize and generalize the objective problems observed during a real vehicle observation experiment and conduct in-depth interviews with participants regarding their usage scenarios, objectives, interactive behaviors, and operational methods related to AVs. By analyzing the factors that influence user acceptance, we will further develop, improve, and integrate the psychological factors affecting drivers' behaviors based on AVs, ultimately creating a model for the influencing factors of user acceptance in AVs.

关键词： Analytical models Correlation Loading Psychology Safety Autonomous vehicles Robots

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A Pedestrian Detection Method for Highways Based on Lightweight FCOS and Hybrid Feature Pyramid Networks

A Pedestrian Detection Method for Highways Based on Lightwei...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： ZhiHeng Cai WeiMing Wang School of Computer Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

With the rapid development of the transportation industry, pedestrian detection algorithms on highway roads have become an important research direction. To solve the problem of a large number of model parameters and a large amount of calculation, this paper proposes a road pedestrian detection method based on lightweight FCOS. In this method, the hybrid feature pyramid networks(HyFpn) is introduced by improving the neck feature pyramid networks, and the lighter mobilenetV3 is used as the backbone network. Experimental results show that the proposed method significantly reduces the size and computational complexity of the model while maintaining high detection accuracy, which is conducive to real-time pedestrian detection on resource-constrained equipment. Compared with the existing methods, the proposed method achieves a better balance between accuracy and speed.

关键词： Training Pedestrians Accuracy Computational modeling Roads Transportation industry Predictive models Feature extraction Mobile handsets Computational complexity

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