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, a...
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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 computervisiontechniques, 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.
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...
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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.
Offshore wind turbines are subjected to highly-varying dynamic loadings and accelerated material degradation, resulting in the need for structural health monitoring, which increases the operation and maintenance cost ...
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Offshore wind turbines are subjected to highly-varying dynamic loadings and accelerated material degradation, resulting in the need for structural health monitoring, which increases the operation and maintenance cost and ultimately the levelized cost of electricity. Recent advances in robotics and intelligentalgorithms offer new opportunities for automated damage assessment that would minimize these costs. This review aims to establish a holistic understanding of robot-based damage assessment technologies and to promote the development and application of these technologies for automated condition assessment of offshore wind turbines. It covers robots as potential carriers of inspection devices, damage inspection approaches, and intelligentalgorithms for damage detection, classification, localization, and quantification for offshore wind turbines. The robots include climbing and underwater varieties, and unmanned aerial vehicles, which carry optical and infrared cameras, and x-ray equipment. Advanced machine learning algorithms for analysis of inspection data are evaluated. Challenges and opportunities of robot-based damage assessment technologies are discussed.
computervision falls under the broad umbrella of artificial intelligence that mimics human vision and plays a vital role in dental imaging. Dental practitioners visualize and interpret teeth, and the structure surrou...
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The design of machine vision applications allows automatic inspection, measuring systems, and robot guidance. Typical applications of industrial robots are based on no-contact sensors to give the robot information abo...
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The design of machine vision applications allows automatic inspection, measuring systems, and robot guidance. Typical applications of industrial robots are based on no-contact sensors to give the robot information about the environment. Robot's machine vision requires photosensors or video cameras to make intelligent decisions about its localization. Video cameras used as image-capturing equipment are too costly in comparison with optical scanning systems (OSS). The OSS system provides spatial coordinates measurements that can be exploited to solve a wide variety of structural problems in real-time. Localization and guidance usingmachine learning (ML) techniques offer advantages due to signals captured can be transformed and be reduced for processing, storage, and displaying. The use of algorithms of ML enhances the performance of the optical system based on localization and guidance. Feature extraction represents an important part of ML techniques to transform the original raw data onto a low-dimensional subspace and holding relevant information. This work presents an improvement of an optical system based on k-nearest neighbor (k-NN) technique to solve the object detection and localization problem. The utility of this improvement allows the optical system can discriminate between the reference source and the optical noise or interference. The OSS system presented in this article has been implemented in structural health monitoring to measure the angular position even under "lighting and weather conditions". The feature extraction techniques used in this article were linear predictive coding (LPC), quartiles (Q(iquartile)), and autocorrelation coefficients (ACC). The results of using k-NN and autocorrelation coefficients and quartiles predicted more than 98% of correct classification by using a reference source light as a class 1 and a light bulb as an optical noise and called class 2.
As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems. Among these transitions-in a bid to red...
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As deep neural networks are spreading to almost all fields, flight systems in the unmanned aerial vehicle (UAV) domain are undergoing various transitions to intelligent systems. Among these transitions-in a bid to reduce flight risk-is the active research domain of autonomous navigation for intelligent UAVs. The autonomous trail-following flight system that this letter introduces can safely consolidate flight control and mission control within the latest commercial hardware platform. The resource usage and degradation of pass-through delay in vision-based convolutional neural network workloads show that virtualisation overhead is not significantly negative, and the overall performance of the introduced system is acceptable. Real-time cooperation is also verified as achievable-in that the workloads incur minimal communication delay-between the controls. Finally, the actual field test analysis demonstrates the applicability of our autonomous UAV system, whereby our system controls the UAV to follow the centre of a set trail.
Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and ma...
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Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computervision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.
Simultaneous localization and mapping (SLAM) is considered to be the basic ability of intelligent mobile robots. In the past few decades, thanks to community’s continuous and in-depth research on SLAM algorithms, the...
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The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve pilot situational awareness and support safe ground movements. Today, pilots deal with increasingly complex cockpit env...
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ISBN:
(数字)9781510649101
ISBN:
(纸本)9781510649101;9781510649095
The Aeronautics industry has pioneered safety from digital checklists to moving maps that improve pilot situational awareness and support safe ground movements. Today, pilots deal with increasingly complex cockpit environments and air traffic densification. Here we present an intelligentvision system, which allows real-time human-machine interaction in the cockpits to reduce pilot's workload. The challenges for such a vision system include extreme change in background light intensity, large field-of-view and variable working distances. Adapted hardware, use of state-of-the-art computervisiontechniques and machine learning algorithms in eye gaze detection allow a smooth, and accurate real-time feedback system. The current system has been over-specified to explore the optimized solutions for different use-cases. The algorithmic pipeline for eye gaze tracking was developed and iteratively optimized to obtain the speed and accuracy required for the aviation use cases. The pipeline, which is a combination of data-driven and analytics approaches, runs in real time at 60 fps with a latency of about 32ms. The eye gaze estimation error was evaluated in terms of the point of regard distance error with respect to the 3D point location. An average error of less than 1.1cm was achieved over 28 gaze points representing the cockpit instruments placed at about 80-110cm from the participants' eyes. The angular gaze deviation goes down to less than 1 degrees for the panels towards which an accurate eye gaze was required according to the use cases.
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...
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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.
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