In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track ...
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In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on *** general,the path is set and can be visible,such as a black line on a white surface with high contrasting *** robot’s path is marked by a distinct line or track,which the robot follows to *** scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based ***,automated map generation,autonomous navigation and path tracking is all becoming more frequent in vision applications.A visual navigation line tracking robot should detect the line with a camera using an image processing *** paper focuses on combining computervisiontechniques with a proportional-integral-derivative(PID)control-ler for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.
Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable au...
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Let an AI and robotics expert help you apply AI, systems engineering, and ML concepts to create smart robots capable of interacting with their environment and users, making decisions, and navigating autonomouslyKey Fe...
ISBN:
(数字)9781805124399
Let an AI and robotics expert help you apply AI, systems engineering, and ML concepts to create smart robots capable of interacting with their environment and users, making decisions, and navigating autonomously
Key Features
Gain a holistic understanding of robot design, systems engineering, and task analysis
Implement AI/ML techniques to detect and manipulate objects and navigate robots using landmarks
Integrate voice and natural language interactions to create a digital assistant and artificial personality for your robot
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Unlock the potential of your robots by enhancing their perception with cutting-edge artificial intelligence and machine learning techniques. From neural networks to computervision, this second edition of the book equips you with the latest tools, new and expanded topics such as object recognition and creating artificial personality, and practical use cases to create truly smart robots. Starting with robotics basics, robot architecture, control systems, and decision-making theory, this book presents systems-engineering methods to design problem-solving robots with single-board computers. You"ll explore object recognition using YOLO and genetic algorithms to teach your robot to identify and pick up objects, leverage natural language processing to give your robot a voice, and master neural networks to classify and separate objects and navigate autonomously, before advancing to guiding your robot arms using reinforcement learning and genetic algorithms. The book also covers path planning and goal-oriented programming to prioritize your robot"s tasks, showing you how to connect all software using Python and ROS 2 for a seamless experience. By the end of this book, you"ll have learned how to transform your robot into a helpful assistant with NLP and give it an artificial personality, ready to tackle real-world tasks an
Efficient robot navigation in operational environments requires precise tracking of the path from the starting point to the destination, typically generated using pre stored map data. However, obstacles in the environ...
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Efficient robot navigation in operational environments requires precise tracking of the path from the starting point to the destination, typically generated using pre stored map data. However, obstacles in the environment can complicate this process, making reliable obstacle avoidance critical for successful navigation. This paper introduces innovative techniques for robotic navigation and obstacle avoidance, specifically designed to assist visually impaired individuals. To mitigate the limitations and inaccuracies inherent in sensor data, we employ sensor fusion algorithms that integrate inputs from infrared, ultrasonic, vision, and tactile sensors. Additionally, visual landmarks are incorporated as reference points to improve internal odometry correction and enhance mapping accuracy. We believe that our approach not only increases the reliability of navigation but also enhances the robot's ability to operate effectively in diverse and challenging conditions.
Multiple object tracking (MOT) is a valuable perception function for robots and intelligent systems. Despite rapid improvements in metrics such as Average Multiple Object Tracking Accuracy (AMOTA) on MOT benchmarks, m...
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ISBN:
(纸本)9798350375039;9798350375022
Multiple object tracking (MOT) is a valuable perception function for robots and intelligent systems. Despite rapid improvements in metrics such as Average Multiple Object Tracking Accuracy (AMOTA) on MOT benchmarks, many trackers are application-specific or run at speeds <1 frames per second (FPS) making them impractical for dynamic tracking applications such as human-robot interaction. To this end, we introduce Modular and Reconfigurable Multiple Object Tracking (MaRMOT), a general-purpose tracking framework for robots implemented in ROS2. Using the nuScenes MOT development kit, we provide accuracy and speed metrics (AMOTA, average FPS, and worst-case FPS) for various tracking methods. We achieve AMOTA of 50.5% with an average of 62.0 FPS on the nuScenes test split, making MaRMOT suitable for online tracking in complex scenes. We demonstrate MaRMOT's modularity on two human tracking applications with different hardware configurations: a 2x object detection camera "smart space" configuration, and a mobile robot configuration with 3D LiDAR detector and an object detection camera. We show that an alternate sensor configuration using human position measurements from a headset improves multiple object tracking accuracy (MOTA) over vision and LiDAR detection alone. Finally, we provide software and hardware design recommendations for tracking applications with tracker speed requirements >10 FPS. MaRMOT is open source and can be extended with new detectors, process models, matching algorithms, and track management techniques.
Deep learning algorithms have led to a series of breakthroughs in computervision, acoustical signal processing, and others. However, they have only been popularized recently due to the groundbreaking techniques devel...
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Deep learning algorithms have led to a series of breakthroughs in computervision, acoustical signal processing, and others. However, they have only been popularized recently due to the groundbreaking techniques developed for training deep architectures. Understanding the training techniques is important if we want to further improve them. Through extensive experimentation, Erhan et al. (2010) empirically illustrated that unsupervised pretraining has an effect of regularization for deep learning algorithms. However, theoretical justifications for the observation remain elusive. In this article, we provide theoretical supports by analyzing how unsupervised pretraining regularizes deep learning algorithms. Specifically, we interpret deep learning algorithms as the traditional Tikhonov-regularized batch learning algorithms that simultaneously learn predictors in the input feature spaces and the parameters of the neural networks to produce the Tikhonov matrices. We prove that unsupervised pretraining helps in learning meaningful Tikhonov matrices, which will make the deep learning algorithms uniformly stable and the learned predictor will generalize fast w.r.t. the sample size. Unsupervised pretraining, therefore, can be interpreted as to have the function of regularization.
With the development of machine vision technology, using machine vision for target localization and measurement is currently a hot research topic to ensure precise positioning of industrial robots. This paper analyzes...
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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 metho...
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Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the *** of the simplest and most efficient algorithms,the artificial potential fi...
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Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the *** of the simplest and most efficient algorithms,the artificial potential field algorithm(APF),may provide real-time navigation in those places but fall into local mini-mum in some *** overcome this problem and to present alternative escape routes for a robot,possible crossing points in buildings may be detected by using object detection and included in the path planning *** study utilized a proposed sensor fusion method and an improved object classification method for detecting windows,doors,and stairs in buildings and these objects were classified as valid or invalid for the path planning *** performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging(LIDAR)sensors to navigate through an unknown closed space and reach a desired goal *** of crossing points allows the robot to escape from areas where it is *** navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF *** results showed that the improved APF methods and the methods rein-forced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same *** the goals outside the current room,traditional APF methods were quite unsuccessful in reaching the *** though improved methods were able to reach some outside targets,the proposed method gave approximately 17%better results than the most success-ful example in achieving targets outside the current *** proposed method can also work in real-time to discover a building and navigate between rooms.
The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. ...
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