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IEEE Conference on Decision and Control

作者： Antonio Acuaviva Jesus Bautista Weijia Yao Juan Jimenez Hector Garcia de Marina Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain Department of Computer Engineering Automation and Robotics Research Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Granada Spain School of Robotics Hunan University

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from field strength measurements at the robot locations and their relative positions concerning the swarm centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the algorithm’s robustness by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots with a generic formation always calculate an ascending direction compensates for the potential loss of individuals.

关键词： Sufficient conditions Shape Focusing Position measurement Robustness Mobile robots Robots

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Autonomous Navigation for Mobile Robots with Sensor Fusion Technology 2nd

Autonomous Navigation for Mobile Robots with Sensor Fusion T...

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2nd International Conference on Industry 4.0 and Advanced Manufacturing, I-4AM 2022

作者： Raja, Vikram Talwar, Dhruv Manchikanti, Akilesh Jha, Sunil Department of Robotics and Automation P.S.G College of Technology Tamil Nadu Coimbatore India IITD-AIA Foundation for Smart Manufacturing New Delhi India Department of Mechanical Engineering IIT Delhi New Delhi India

ISBN: (纸本)9789811905605

Autonomous navigation for a mobile robot is a method used to localize and navigate a robot in a random environment. The existing solution leverages a 2D LiDAR on top of the robot for mapping and obstacle avoidance but the LiDAR at times is unable to detect objects due to their size and color properties. This paper aims to develop a robot that uses both LiDAR and a vision-based sensor as fused sensor technology and to navigate the robot in any direction without any constraints using four-wheel Mecanum drive. The method uses 2D LiDAR as the global sensor for mapping and obstacle avoidance and fusing it with a vision sensor for detecting objects which cannot be detected by LiDAR. The vision sensor uses a deep learning object detection method;YOLOv3. Whenever an object is detected by a neural network the program sends a false laser scan, which is updated in the local cost map of the ROS navigation stack, which makes the robot aware of an object ahead and takes an alternate path to reach the goal position. This robot was tested in a gazebo ROS environment and was able to avoid obstacles irrespective of the size or color of the object. This method was successful in a simulation environment and can be used in industries that need 2D LiDAR as an affordable option and as a replacement for 3D LiDAR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-conditioned Human Mtion Prediction

THÖR-Magni: Comparative Analysis of Deep Learning Models fo...

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2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023

作者： De Almeida, Tiago Rodrigues Rudenko, Andrey Schreiter, Tim Zhu, Yufei Maestro, Eduardo Gutierrez Morillo-Mendez, Lucas Kucner, Tomasz P. Martinez Mozos, Oscar Magnusson, Martin Palmieri, Luigi Arras, Kai O. Lilienthal, Achim J. Örebro University Sweden Corporate Research Robert Bosch GmbH Stuttgart Germany Aalto University Mobile Robotics Group Department of Electrical Engineering and Automation Finland Finnish Center for Artificial Intelligence Fcai Finland

ISBN: (纸本)9798350307443

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THÖR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. © 2023 IEEE.

关键词： deep learning human motion dataset human trajectory prediction

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Vertical Air Gap Integration in Micromachined Millimeter-Wave Patch Antenna with Inset-Fed Technique for RF Enhancement

Vertical Air Gap Integration in Micromachined Millimeter-Wav...

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Advancement in Technology (ICONAT), International Conference for

作者： B Badrinathan G Anitha Department of Automation and Robotics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

This work proposes an innovative antenna design for millimeter-wave applications. For improved performance, the proposed antenna employs a bulk micromachining structure combined with an inset-fed technique. The design is fully simulated using the HFSS software using a substrate material having a dielectric constant of 12.9. A vertical air gap is introduced beneath the patch element through the simulated micromachining structure to enhance the RF performance of the antenna. The designed antenna dimensions are $1.2 \mathrm{~mm} \times 1.7 \mathrm{~mm} \times 0.27 \mathrm{~mm}$ and are intended to function at $\mathbf{6 0 ~ G H z}$ within the millimeter-wave frequency spectrum. Outstanding performance metrics are shown by the simulation results, such as a maximum gain of 4.82 dBi, a return loss of -60.95 dB and a voltage standing wave ratio (VSWR) of 1 at the desired operating frequency of 60 GHz. The simulated design of the antenna exhibits promising potential for integration into emerging millimeterwave wireless systems and devices.

关键词： Radio frequency Wireless communication Wet etching Patch antennas Millimeter wave technology Air gaps Micromachining Antennas Surface treatment Substrates

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Control Simulation of Proportional-Integral and Sliding Mode Control for Precision Seed Planters

Control Simulation of Proportional-Integral and Sliding Mode...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Yasir Nawaz Muhammad Usman Qadir Muhammad Awais Khan Izhar Ul Haq Kamran Shah Tahir Khan Department of Mechatronics Advanced Robotics & Automation Lab University of Engineering and Technology Peshawar Pakistan

The desired development and design of an electric control system (ECS) for a precision planter is one of the most crucial elements in evaluating the efficacy of applicators for plant production. The seeds would be sown using this approach. Years have been invested in the continuous development of a range of application methods and metering systems, each of which offers specific advantages for applying the required system with a high degree of precision. Researchers worldwide have been trying to make electric-driven seed meters (EDSM) for planters, but they haven't succeeded. This study looks into the current state of installing seed metering systems for precision planters. Proportional-integral (PI) and sliding mode control (SMC) designs of the control system have been implemented to improve the planting quality of the planters. We want to highlight the electrically driven control system (EDCS) using these designs. An overview of the noteworthy features and limitations of past research is given. We also discuss several future directions for future research, concentrating on the knowledge gaps in this area.

关键词： Meters Industries PI control automation System performance Production Robustness

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Implementation of Offset Injection PWM Technique for Grid Integration

Implementation of Offset Injection PWM Technique for Grid In...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Renuka Devi, G. Yuvaraja, S. Syed Abdul Salam, M. Department of Robotics and Automation Manakula Vinayagar Institute of Technology Puducherry Kalitheerthalkuppam India Department of Electronics and Communication Engineering M.A.M. College of Engineering & Technology Tamil Nadu Trichy India

ISBN: (纸本)9789811670107

This paper addresses the offset injection-based PWM technique for grid integration. In the MATLAB/Simulink environment, the grid-tied inverter is developed. The sinusoidal pulse width grid-tied inverter is developed and studies the performance in terms of output current and THD. Compared to SPWM technique, the offset injection PWM technique provides low harmonic current, and power factor is improved, enhancing power management capability, improving storage capacity and to extract maximum power. The proposed offset injection is presented in steady-state and dynamic conditions. The existing and proposed technique performance is analyzed in different step change conditions and identifies the method suitable for grid integration. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pulse width modulation

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Learning to Hop for a Single-Legged Robot with Parallel Mechanism

arXiv

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arXiv 2025年

作者： Zhang, Hongbo Chu, Xiangyu Chen, Yanlin Tang, Yunxi Yue, Linzhu Liu, Yun-Hui Au, Kwok Wai Samuel Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Multiscale Medical Robotics Centre Hong Kong

This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, learning to hop suffers from prolonged aerial phase and the sparse nature of the rewards. To address them, we propose a learning framework to encode long-history feedback to account for the under-actuation brought by the prolonged aerial phase. In the proposed framework, we also introduce a simplified serial configuration for the parallel design to avoid directly simulating parallel structure during the training. A torque-level conversion is designed to deal with the parallel-serial conversion to handle the sim-to-real issue. Simulation and hardware experiments have been conducted to validate this framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery

Natural Disaster Damage Assessment using Semantic Segmentati...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muhammad Haroon Asad Malik Muhammad Asim Muhammad Naeem Mumtaz Awan Muhammad Haroon Yousaf Computer Engineering Department University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation University of Engineering and Technology Taxila Pakistan

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results.

关键词： Industries Visualization Semantic segmentation Public infrastructure Random access memory Transformers Autonomous aerial vehicles

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Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

Real-Time Plant Recognition and Crop Row Navigation for Auto...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Fazal Nasir Muhammad Haris Bilawal Khan Muhammad Tufail Muhammad Tahir Khan Zhang Dong Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments.

关键词： Service robots Plants (biology) Spraying Crops Wheels Robot sensing systems Visual servoing

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