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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Anita Joshi Tanishq Thuse Prashant Thorve Raj Thakur Anshul Thote Isha Thakur Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Recent advancements in artificial intelligence and robotics have transformed the retail industry, leading to innovative solutions that enhance the shopping experience. Our paper presents the High-Tech Shopping Cart, an intelligent system that leverages object detection and human-following technology to provide a seamless, hands-free shopping experience. The cart utilizes state-of-the-art object detection algorithms to identify and track items selected by shoppers while employing a robust human-following mechanism to navigate autonomously through stores. Our system integrates computer vision techniques, sensor fusion, and advanced path-planning algorithms within a modular framework, featuring cameras and depth sensors for real-time processing. Extensive experiments demonstrate high accuracy in object detection and reliable navigation in complex environments. User satisfaction surveys indicate a positive reception, highlighting the cart's potential to significantly enhance the shopping experience.

关键词： Surveys Navigation Service robots Object detection Sensor fusion Robot sensing systems Robustness Real-time systems Object recognition intelligent systems

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Comparative Analysis of Reinforcement Learning techniques in Simulated Robotics Environments

Comparative Analysis of Reinforcement Learning Techniques in...

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IEEE International Conference of intelligent Robotic and Control Engineering (IRCE)

作者： Abey Yoseph M. Hassan Tanveer Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Department of AI and Data Science National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. The implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. The Python openai_ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. The agent’s action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. The reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. The algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. The goal state at each episode was randomly generated and the robot’s performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal.

关键词： Training Measurement Q-learning Machine learning algorithms Operating systems Robot vision systems Turning Prediction algorithms Libraries robots

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PLSAV: Parallel loop searching and verifying for loop closure detection

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IET intelligent TRANSPORT SYSTEMS 2021年第5期15卷 683-698页

作者： Yang, Zhe Pan, Yun Deng, Lei Xie, Yuan Huan, Ruohong Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

Visual simultaneous localization and mapping (vSLAM), one of the most important applications in autonomous vehicles and robots to estimate the position and pose using inexpensive visual sensors, suffers from error accumulation for long-term navigation without loop closure detection. Recently, deep neural networks (DNNs) are leveraged to achieve high accuracy for loop closure detection, however the execution time is much slower than those employing handcrafted visual features. In this paper, a parallel loop searching and verifying method for loop closure detection with both high accuracy and high speed, which combines two parallel tasks using handcrafted and DNN features, respectively, is proposed. A fast loop searching is proposed to link the bag-of-words features and histogram for higher accuracy, and it splits the images into multiple grids for high parallelism;meanwhile, a DNN feature extractor is utilized for further verification. A loop state control method based on a finite state machine to control these tasks is designed, wherein the loop closure detection is described as a context-related procedure. The framework is implemented on a real machine, and the top-2 best accuracy and fastest execution time of 80-543 frames per second (min: 1.84ms, and max: 12.45ms) are achieved on several public benchmarks compared with some existing algorithms.

关键词： Image recognition Mobile robots Automata theory computer vision and image processing techniques Machine learning (artificial intelligence) Neural nets

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IoT-Based Automation System for Vertical Hydroponic Farms 26

IoT-Based Automation System for Vertical Hydroponic Farms

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kumburage, D.K. Hewavitharana, H.V.T.G. Perera, M.N.T. Amarasinghe, Y.W.R. Jayathilaka, W.A.D.M. University of Moratuwa Dept. of Mechanical Engineering Sri Lanka

ISBN: (纸本)9798331521950

Greenhouse vertical rack hydroponic systems offer a sustainable and efficient solution for meeting the increasing global food demand. This paper introduces an IoT-integrated automated system designed to perform labor intensive and repetitive tasks in hydroponic farming. The proposed system integrates a robotic platform for transplanting and inspecting plants, as well as an intelligent controller to control and monitor greenhouse conditions and nutrient solution parameters. To enhance performance and accessibility, the system is integrated with an IoT platform, which includes a user-friendly web interface, enabling remote monitoring and control. Key features such as computer vision techniques and advanced control algorithms are implemented to maximize operational efficiency. Experimental results validate its ability to reduce labor, improve productivity, and ensure consistent crop quality. This solution highlights the potential for scalable and sustainable advancements in modern agriculture. © 2025 IEEE.

关键词： intelligent robots

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On the Development of intelligent Autonomous Agricultural Robot

On the Development of Intelligent Autonomous Agricultural Ro...

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Advances in Science and Engineering Technology International Conferences (ASET)

作者： Abdel Ilah N. Alshbatat Moath Awawdeh Department of Communication Electronic and Computer Engineering Tafila Technical University Tafila Jordan Department of Electrical Engineering Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

This study discuss the development of an intelligent autonomous agricultural robot, designed to detect and treat diseases affecting herbaceous medicinal plants using artificial intelligence (AI) techniques. Herbaceous plants are particularly prone to diseases and require regular monitoring due to their rapid growth and short flowering periods. Leveraging technologies such as Internet of Things (IoT) and artificial intelligence, farmers can now efficiently identify and address plant diseases at early stage. In this work, we present an intelligent autonomous robot capable of proactively identifying and treating herbaceous diseases as a preventive measure. We developed a robust algorithm to control the robot's movements within predefined areas inside plant greenhouses. Through the use of Pixy camera, the robot can detect plant yellowing, prompting it to halt and direct a nozzle towards the identified plant, initiating a preventive spraying process at predetermined times and positions. Furthermore, the robot is equipped with a companion computer responsible for disease identification using deep learning algorithms. This identification capability enables farmers to make immediate decisions and initiate disease treatments promptly. The entire system was developed, implemented, and initially tested outside our labs. The testing results corroborate the system's practicality and demonstrate that the prototype may be simply implemented to provide an added-value to the farmers of herbaceous medicinal plants.

关键词： Medicinal plants Agricultural robots Plant diseases Robot vision systems Spraying Prototypes Internet of Things Monitoring Diseases Testing

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IoT-Based Automation System for Vertical Hydroponic Farms

IoT-Based Automation System for Vertical Hydroponic Farms

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IEEE International Conference on Industrial Technology (ICIT)

作者： D. K. Kumburage H. V. T. G. Hewavitharana M. N. T. Perera Y. W. R. Amarasinghe W. A. D. M. Jayathilaka Dept. of Mechanical Engineering University of Moratuwa Sri Lanka

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

关键词： Productivity Automation Service robots Green products Hydroponics Agriculture Real-time systems Optimization Remote monitoring Farming

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Deep Learning-Based Visual Navigation algorithms for Mobile robots: A Comprehensive Study

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Journal of Computing and Information Technology 2022年第4期30卷 257-273页

作者： Yu, Wei Tian, Xinzhi Xi'an Siyuan University Shaanxi Xi'an China

This research addresses the challenges faced by mobile robots in efficiently navigating complex environments. A novel approach is proposed, leveraging deep learning techniques, and introducing the Neo model. The method combines Split Attention with the ResNeSt50 network to enhance the recognition accuracy of key features in the observed images. Furthermore, improvements have been made in the loss calculation method to improve navigation accuracy across different scenarios. Evaluations conducted on AI2THOR and active vision datasets demonstrate that the improved model achieves higher average navigation accuracy (92.3%) in scene 4 compared to other methods. The success rate of navigation reached 36.8%, accompanied by a 50% reduction in ballistic length. Additionally, compared to HAUSR and LSTM-Nav, this technology significantly reduced collision rates to 0.01 and reduced time consumption by over 8 seconds. The research methodology addresses navigation model accuracy, speed, and generalization issues, thus making significant advancements for intelligent autonomous robots. ACM CCS (2012) Classification: Computing methodologies → Machine learning → Machine learning algorithms Artificial intelligence → computer vision → vision for robotics. © 2022, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.

关键词： Navigation

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A Comprehensive Survey of Image Augmentation techniques for Deep Learning

arXiv

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arXiv 2022年

作者： Xu, Mingle Yoon, Sook Fuentes, Alvaro Park, Dong Sun Department of Electronics Engineering Jeonbuk National University Jeonbuk54896 Korea Republic of Department of Computer Engineering Mokpo National University Jeonnam58554 Korea Republic of Core Research Institute of Intelligent Robots Jeonbuk National University Jeonbuk54896 Korea Republic of

Although deep learning has achieved satisfactory performance in computer vision, a large volume of images is required. However, collecting images is often expensive and challenging. Many image augmentation algorithms have been proposed to alleviate this issue. Understanding existing algorithms is, therefore, essential for finding suitable and developing novel methods for a given task. In this study, we perform a comprehensive survey of image augmentation for deep learning using a novel informative taxonomy. To examine the basic objective of image augmentation, we introduce challenges in computer vision tasks and vicinity distribution. The algorithms are then classified among three categories: model-free, model-based, and optimizing policy-based. The model-free category employs the methods from image processing, whereas the model-based approach leverages image generation models to synthesize images. In contrast, the optimizing policy-based approach aims to find an optimal combination of operations. Based on this analysis, we believe that our survey enhances the understanding necessary for choosing suitable methods and designing novel algorithms. © 2022, CC BY-NC-ND.

关键词： computer vision

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Towards vision-Based Impedance Control for the Contact Inspection of Unknown Generically-Shaped Surfaces with a Fully-Actuated UAV

Towards Vision-Based Impedance Control for the Contact Inspe...

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IEEE/RSJ International Conference on intelligent robots and Systems (IROS)

作者： Rashad, Ramy Bicego, Davide Jiao, Ran Sanchez-Escalonilla, Santiago Stramigioli, Stefano Univ Twente Robot & Mechatron Grp Enschede Netherlands Beihang Univ Sch Mech Engn & Automat Beijing Peoples R China ITMO Univ St Petersburg Russia

ISBN: (纸本)9781728162126

The integration of computer vision techniques for the accomplishment of autonomous interaction tasks represents a challenging research direction in the context of aerial robotics. In this paper, we consider the problem of contact-based inspection of a textured target of unknown geometry and pose. Exploiting state of the art techniques in computer graphics, tuned and improved for the task at hand, we designed a framework for the projection of a desired trajectory for the robot end-effector on a generically-shaped surface to be inspected. Combining these results with previous work on energy-based interaction control, we are laying the basis of what we call vision-based impedance control paradigm. To demonstrate the feasibility and the effectiveness of our methodology, we present the results of both realistic ROS/Gazebo simulations and preliminary experiments with a fully-actuated hexarotor interacting with heterogeneous curved surfaces whose geometric description is not available a priori, provided that enough visual features on the target are naturally or artificially available to allow the integration of localization and mapping algorithms.

关键词： vision-Based Impedance Control Aerial Physical Interaction Contact-Based Inspection Aerial Robotics

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The Design of IR System for PC Systems

The Design of IR System for PC Systems

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Jason Gunjan Chhabra Mohammed Al Sailh France Laith H. Jasim Abdul Redha Hussein Sabr Doaa Talib Zaidan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Tareq Hafdhi Abdtawfeeq Dhi Qar Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. This synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply with the rules. The system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.

关键词： Service robots Heuristic algorithms Robot vision systems Collaboration Entertainment industry Games Kinematics

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