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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Ramsha Shahid Anam Baloch Humayun Tahir Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan Department of Electronic Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Terrain adaptability gives snake robots an edge over wheeled mobile robots. Snake robot's applications expansion to disaster management is due to its limbless and modular design. Replicating the navigation and locomotion of a biological snake have been in the spotlight for many researchers. In this study, a robust localization algorithm of a snake robot is developed. A sensor fusion-based algorithm using probabilistic approaches: UKF (Unscented Kalman Filter) and EKF (Extended Kalman Filter) has been proposed. Odometry is fused with one and two IMUs (Inertial Measurement Units) with both probabilistic approaches. Evaluation of results in a simulation environment showed that the fusion of two IMUs and odometry using EKF outer performs in terms of accuracy when odometry is fused with one IMU using EKF. Furthermore, the fusion of two IMUs and odometry with UKF is computationally expensive resulting in a large convergence time which is not the best suitable approach that can be utilized for the snake robot.

关键词： Location awareness Industries Measurement units Snake robots Inertial navigation Disaster management Robot sensing systems

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Deep Learning-Based Approach for Frost Quantification in Domestic Refrigerators

Deep Learning-Based Approach for Frost Quantification in Dom...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Hassan Akbar Huzzam Hassan Anjum Naeem Malik Tahir Habib Nawaz National Centre of Robotics and Automation Department of Mechatronics Engg. CEME NUST Islamabad Pakistan Department of Mechatronics Engg. CEME NUST Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Frost formation in refrigerators disrupts heat ex-change, lowering system efficiency and causing operational is-sues, making defrosting essential for maintaining performance. However, modern defrost systems rely on sensors for frost detection. The sensory approach is reliable but it could degrade over time in changing environmental conditions. To address this limitation, the paper proposes a real-time frost quantification method using digital image processing and deep learning. A custom Convolutional Neural Network (CNN) model is designed to classify frost thickness in RGB images captured inside a refrigerator. We have used a custom dataset, collected from a real refrigeration environment and made a comparison with other methods previously used for frost thickness estimation. The dataset is collected from several experiments to ensure its diversity such as illumination and environmental changes. This method enables precise frost detection, targeted for optimal defrost control for improved efficiency. Results indicate that the CNN model achieves a high test accuracy of 98.10%. The model's performance surpasses traditional methods, demonstrating its robustness in distinguishing varying frost levels from 0 to 4 (mm). This method enables precise frost thickness estimation and is a suitable choice for defrost control to improve the energy efficiency of the refrigerator.

关键词： Deep learning Accuracy Thresholding (Imaging) System performance Estimation Lighting Sensor systems Sensors Refrigeration Convolutional neural networks

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Design and Development of Autonomous Mobile Robot for Mapping and Navigation System

Design and Development of Autonomous Mobile Robot for Mappin...

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IEEE Punecon

作者： B Kiran S Karthikeyan M A Suhel Pasha K N Manjunatha S Manoj Kumar Sharvin Vivian Moras Department of Robotics and Automation JAIN (Deemed-to-be University) Bengaluru India

ISBN: (纸本)9781665498982

Mobile Robot for Mapping and Navigation is one of the challenging researches in the area of robotics. An unknown indoor and outdoor mapping in dynamic environment is used for navigation of robot. This research focuses on mapping utilizing a GMapping approach that uses simultaneous localization and mapping (SLAM). The concept of navigation or autonomous driving is used in SLAM it helps for mobile robot to move independently. SLAM and RVIZ GMapping software that is available an open source, along with the Robotic Operating System (ROS) and RPLiDAR camera, is required to map an unknown dynamic indoor environment. The laser data and navigation messages are passed to mapping in an unknowable environment, the SLAM GMapping function is taken into account. The command for autonomous navigation will be designed and delivered by the navigation stack after the mapping has been generated.

关键词： Simultaneous localization and mapping Navigation Service robots Urban areas Wheels Virtual environments Software

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Adaptive Neural Network Tracking for a Magnetic Microrobot in Presence of Model Uncertainty and Unknown Disturbance 19

Adaptive Neural Network Tracking for a Magnetic Microrobot i...

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19th IEEE International Conference on automation Science and Engineering, CASE 2023

作者： Wang, Haoyu Liu, Yueyue Fan, Qigao Wu, Xiaoyu Hu, Yingbai School of Internet of Things Engineering Institute of Automation Jiangnan University Wuxi214122 China National University of Singapore Department of Biomedical Engineering 119077 Singapore Multi-Scale Medical Robotics Centre The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350320695

Magnetic microrobots have potential applications in many medical diagnosis and treatment applications due to the unique characteristics in biological fine manipulation, such as precise drug delivery and being able to be used in complex biological environments. Precise and robust trajectory tracking control of magnetic microrobots under complex disturbances and uncertain dynamic model is still one of the most important and challenging steps in achieving ideal applications. In this paper, an adaptive neural network control algorithm is proposed to address the uncertain dynamic parameters and complex external disturbances. The developed state feedback control strategy based on a radial basis function neural network (RBFNN) can effectively estimate the uncertainties online from the state and desired trajectories, rather than the model-based control. The controller require as little knowledge of system parameters as possible. Subsequently, the closed-loop stable system is guaranteed based on Lyapunov theory. Finally, the two experimental researches are conducted on our developed electromagnetic manipulation system to validate the effectiveness of the proposed controller. © 2023 IEEE.

关键词： Controllers

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Enhanced Multi-Occupant Tracking Via Fusion of Low-Intrusive Radar and Wearable Ultra-Wideband Devices Using Autoencoders

SSRN

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SSRN 2024年

作者： Polo-Rodríguez, Aurora Anguita-Molina, Miguel Ángel Ruiz, Ignacio Rojas Medina-Quero, Javier Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Department of Computer Science University of Jaén Jaén23071 Spain

This work explores the integration of millimetre wave (mmWave) Radar and minimal configuration of Ultra-Wideband (UWB) devices for enhanced tracking of multiple occupants in real domestic environments. Using a low-cost, non-intrusive, and quickly deployable device setup, our approach addresses the challenges of multi-occupant tracking with a focus on privacy and ease of installation. Initially, mmWave Radar identifies the presence of occupants with high precision but lacks individual recognition and limited sensitivity. By incorporating a minimal configuration of UWB (wearable tags and ambient anchors), we include identification through signal strength measurements, bypassing complex localisation needs. In addition, we propose several autoencoder models to fuse data from these diverse sources. Experiments carried out in two real-world domestic settings that involve up to three inhabitants per room showcase the ability of the proposal to combine mmWave and UWB technologies for indoor multioccupant tracking, suggesting promising applications in smart homes, ambient assisted living, and eHealth. © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

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Investigate the Stability of a Polylactic acid 3D Printing Unmanned Aerial Vehicle

Investigate the Stability of a Polylactic acid 3D Printing U...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Pedklah Kamonsukyunyong Watcharasak Phowangdon Suriyachat Yooyen Theputis Sarathai Soravit Sriviriyaporn Somboon Sukpancharoen Department of Agricultural Engineering Khon Kaen University Khon Kaen Thailand Department of Automation Robotics and Intelligent System Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

This study investigates the influence of arm length on the stability of quadrotors, a type of unmanned aerial vehicle (UAV) widely used in various applications. The research employs 3D printing technology to fabricate quadrotors with arm lengths of $20 \mathrm{~cm}, 25 \mathrm{~cm}$, and 30 cm, using polylactic acid (PLA) as the primary material. The quadrotors are designed using advanced 3D modeling software and printed with a Creality Sermoon D1 3D printer. Stability tests are conducted in a controlled indoor environment, measuring the angular deviation and other flight parameters during hover and basic maneuvers. The results demonstrate that the 30 cm arm length configuration exhibits the highest stability, significantly outperforming the 20 cm and 25 cm configurations. The findings highlight the importance of arm length optimization in quadrotor design and discuss the implications for various industries. However, future research should investigate the influence of other design parameters and their interactions with arm length to develop a more comprehensive understanding of UAV design optimization.

关键词： Industries Three-dimensional displays Torque Service robots Autonomous aerial vehicles Three-dimensional printing Stability analysis Software Steady-state Quadrotors

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Acquiring feedback into the manufacturing process design from Industrial policies

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Procedia CIRP 2024年 128卷 579-584页

作者： Alexios Papacharalampopoulos Manolis Toupalis Panagiotis Lagios Olga Maria Karagianni Panagiotis Stavropoulos Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras 26504 Greece Gizelis Robotics Schimatari Viotias 32009 Kormatzini Area Greece

The regulatory and social framework of European industry can be demanding, i.e. in terms of achieving different strategies, such as Industry 4.0 and Industry 5.0. To study this co-existence of strategies, a circular cell is utilized herein. It has been implemented based on a specific design, meeting requirements for an automotive microfactory and under the concept of circular economy. The objective of the cell has been to address resources efficiency and partial digitalization of workflows towards human-in-the-loop remanufacturing parts of electric vehicles. The process chain includes removal and joining processes, the processed part has been of medium complexity, and the workflow has engaged several digital tools, as a result of the quality monitoring requirements. The cell has been operational and various qualitative metrics have been elaborated. Subsequently, feedback is extracted towards the design for future applications pertaining to both circular economy and Industry 5.0. As such, in addition to the environmental sustainability implied by the resources efficiency, the production resilience and the human-centricity of the jobs involved are also regarded as extra requirements. Also, the connection to the ecosystem is taken into consideration. Finally, guidelines for future designs are also extracted.

关键词： Manufacturing process Cell Industry 5.0 application design criteria

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Towards Robo-Coach: Robot Interactive Stiffness/Position Adaptation for Human Strength and Conditioning Training

Towards Robo-Coach: Robot Interactive Stiffness/Position Ada...

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IEEE International Conference on robotics and automation (ICRA)

作者： Chenzui Li Xi Wu Tao Teng Sylvain Calinon Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Idiap Research Institute Martigny Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Traditional strength and conditioning training relies on the utilization of free weights, such as weighted implements, to elicit external stimuli. However, this approach poses a significant challenge when attempting to modify or adjust the loads within a single training set. This paper introduces an innovative method for achieving adjustable loads during resistance training by leveraging physical Human-Robot Interaction (pHRI). The primary objective is to regulate targeted muscle activation through the use of Robo-Coach (robotic coach system). We first utilize a Task-Parameterized Gaussian Mixture Model (TP-GMM) to learn the motion of coach demonstration, which can be generalized for the trainees. The 3D path extracted from the generated trajectory is then projected onto a 2D plane with respect to the direction of the load. Furthermore, we propose a hybrid stiffness/position generator for online task execution. This generator determines the desired positions in the 2D plane according to the contact point displacements in the stimuli direction and, simultaneously, sets the desired stiffness based on the muscle activation feedback. Finally, the Robo-Coach is implemented with a variable impedance controller to achieve load-adjustable resistance training with the trainee. The biceps curl exercises were conducted and the results showed favorable performance, indicating the effectiveness of this approach.

关键词： Training Three-dimensional displays Human-robot interaction Muscles Generators Trajectory Impedance

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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2024 China automation Congress, CAC 2024

作者： Zhang, Haozhe Ma, Liyan Ying, Shihui Institute of Artificial Intelligence Shanghai University Shanghai200444 China School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368604

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects. © 2024 IEEE.

关键词： Active learning

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A Dog Harness Product Design Assessment Using EDAS Method 23th

A Dog Harness Product Design Assessment Using EDAS Method

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Vadivel, S.M. Alli, P. Vinoth Babu, N. Kumar, Vimal Abhinav, A. VIT Business School Chennai Vandalur Kelambakkam Road Chennai600127 India Department of Robotics and Automation Rajalakshmi Engineering College Thandalam Chennai602105 India Chaoyang University of Technology Taichung County41349 Taiwan VIT Law School Vandalur Kelambakkam Road Chennai600127 India

ISBN: (纸本)9783031648496

A smart dog harness is a wearable device that integrates technology to enhance the safety, health, and well-being of dogs. This device incorporates various features such as GPS tracking, activity monitoring, and health sensors that can provide pet owners with real-time data about their dog’s movements, health status, and behavior. The smart dog harness is designed to be comfortable, durable, and easy to use, with features like LED lights for visibility in low light conditions, a panic button that alerts the owner if the dog is lost, and an app that allows the owner to monitor the dog’s activity remotely. The technology used in a smart dog harness is rapidly evolving, and as more pet owners seek ways to enhance their dog’s care, it is likely that these devices will become increasingly popular. Ultimately, a smart dog harness can be an invaluable tool for any pet owner who wants to keep their furry friend safe, healthy, and happy. Here we are applying some product design techniques like prototype design models analyze using EDAS method for overall designing a smart dog harness. For that purpose, we have analyzed comfortability, durability, ease of use, price, health monitoring, safety features and behavioral feedback under each existing and our product prototype model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Product design

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