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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXXII - Algorithms and Techniques"

共 372 条记录，以下是11-20 订阅

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based Learning

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Logesh G S. Vinoth Nagarajan Pitchandi A.G. Karthikeyan Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick-and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency.

关键词： Point cloud compression Accuracy Service robots Robot kinematics Pose estimation Object detection Manuals Calibration Reliability Principal component analysis

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Reexamining Indoor Mobile robots from a Cognitive Perspective

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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Robotics and computer vision (ICRCV), International conference on

作者： Fei Song Jinyu Li Fengzhen Tang Yandong Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.

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Hy-Tech Shopping Cart Using Object Detection and Human Following Technology

Hy-Tech Shopping Cart Using Object Detection and Human Follo...

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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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AI-based vision system for collision detection in HRC applications 9

AI-based vision system for collision detection in HRC applic...

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9th CIRP conference on Assembly Technology and Systems, CATS 2022

作者： Makris, Sotiris Aivaliotis, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

Human-Robot Collaboration (HRC) enabling mechanisms require real-time detection of potential collisions among human and robots. Taking under consideration the already existing standards and the literature, most of collision detection techniques require the integration of sensorial systems on the robot aiming to identify the contact events. This paper deals with a novel approach for the identification of human and robot collision based on vision systems. Moreover, Artificial intelligent (AI) algorithms are required to classify the captured data near real-time and to provide a score about the collision status (contact or non-contact) between a human and the robot. Accordingly, the AI models should be trained using the appropriate image data enabling an accurate classification. The proposed system has been developed in a lab environment. A detailed presentation of the system implementation, its performance and the potential integration in a real industrial environment are discussed in this paper. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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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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IoT-Based Automation System for Vertical Hydroponic Farms 26

IoT-Based Automation System for Vertical Hydroponic Farms

引用

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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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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The Design of IR System for PC Systems

The Design of IR System for PC Systems

引用

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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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...

引用

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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Autonomous oil spill and pollution detection for large-scale conservation in marine eco-cyber-physical systems

Autonomous oil spill and pollution detection for large-scale...

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International conference of Signal Processing and intelligent Systems (ICSPIS)

作者： Asma Bahrani Babak Majidi Mohammad Eshghi Khatam University Tehran Iran Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665409391

In recent years, advancement of the industry and increased human activities created significant pollution in the marine environment and coastal regions of the Persian Gulf. These pollutions cause various diseases and serious damages to the human health and animal species. Early identification of various pollutions helps the coastal management to organize their resources and rapidly respond to the problems. Due to the large scale of the coastal regions, manual investigation of the pollutions is a very time-consuming task. Unmanned robots can be used as autonomous agents for rapid large-scale detection and classification of pollutions in the coastal regions. In this paper, an artificial intelligence-based vision system for autonomous marine pollution detection is proposed. A combination of computer vision and machine learning methods are used for autonomous detection of various pollutions in the coastal and marine environment. In this study, 3000 images of Persian Gulf coastal pollutions is collected and used for training an artificial intelligence system for coastal conservation. The experimental results shows that the proposed framework has a 98% accuracy for identifying and classifying coastal and marine pollutions. The proposed system can be used as the vision system of an autonomous coastal conservation robot and increase the speed of coastal conservation and management significantly.

关键词： Pollution Machine learning algorithms Marine pollution Environmentally friendly manufacturing techniques Machine vision Sea measurements Signal processing algorithms

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