Within the area of environmental perception, automatic navigation, object detection, and computervision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term vis...
详细信息
Within the area of environmental perception, automatic navigation, object detection, and computervision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computervision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computervision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999-2024), with a primary focus on the technical advancement in computervision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computervisiontechniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computervision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligentrobots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computervisionalgorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends.
Existing studies on indoor position recognition employ diverse evaluation methods, which complicates direct accuracy comparisons across techniques. To address this issue, this study proposes a novel framework for eval...
详细信息
Existing studies on indoor position recognition employ diverse evaluation methods, which complicates direct accuracy comparisons across techniques. To address this issue, this study proposes a novel framework for evaluating the accuracy of indoor position recognition methods. The proposed framework evaluates accuracy by using the position recognition results of a grid-pattern-tracking autonomous mobile robot (GPT-AMR) as a benchmark. To validate the proposed evaluation method, a comparative analysis was conducted on four position recognition algorithms: (1) a computervision-based algorithm, (2) a Bluetooth Low Energy (BLE)-based trilateration algorithm, (3) a BLE-based adaptive trilateration algorithm, and (4) a least squares method (LSM)-based algorithm. Experimental results demonstrated that the proposed evaluation method, which employs GPT-AMR, offers improved speed, accuracy, and practical applicability compared to conventional approaches. Furthermore, this method enables objective comparisons and evaluations of a wide range of indoor position recognition technologies, including both computervision- and BLE-based algorithms, using a standardized criterion. Future research will focus on systematically validating the generalizability of the proposed method across different indoor environments and operational conditions. This study aims to advance indoor position recognition technology for autonomous mobile robots (AMRs) and improve their applicability in various service robotics domains.
In robotics, route planning is essential to ensure the safe and efficient movement of robots within the workplace. This process involves determining a trajectory, usually a series of points in the workspace, to achiev...
详细信息
ISBN:
(纸本)9798331522759;9798331522742
In robotics, route planning is essential to ensure the safe and efficient movement of robots within the workplace. This process involves determining a trajectory, usually a series of points in the workspace, to achieve a specific goal. It is essential to consider criteria such as reducing the length of the route, the number of manoeuvres and the avoidance of obstacles. Route planning techniques generally require modelling the environment, representing both the structure and the obstacles (fixed or mobile), and the implementation of algorithms that generate the trajectory through the free areas of the environment. This approach often includes constructing a graph of possible trajectories and using minimum path search algorithms, such as A*. This article presents a route planning algorithm that uses Voronoi diagrams and uses artificial visionalgorithms. In addition, a case study is described in which the proposed technique is applied to guide an automated system through a maze drawn on a whiteboard by a user.
computervision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Tech...
详细信息
Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software al...
详细信息
Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare.
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...
详细信息
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
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...
详细信息
In robotics, route planning is essential to ensure the safe and efficient movement of robots within the workplace. This process involves determining a trajectory, usually a series of points in the workspace, to achiev...
详细信息
ISBN:
(数字)9798331522742
ISBN:
(纸本)9798331522759
In robotics, route planning is essential to ensure the safe and efficient movement of robots within the workplace. This process involves determining a trajectory, usually a series of points in the workspace, to achieve a specific goal. It is essential to consider criteria such as reducing the length of the route, the number of manoeuvres and the avoidance of obstacles. Route planning techniques generally require modelling the environment, representing both the structure and the obstacles (fixed or mobile), and the implementation of algorithms that generate the trajectory through the free areas of the environment. This approach often includes constructing a graph of possible trajectories and using minimum path search algorithms, such as A*. This article presents a route planning algorithm that uses Voronoi diagrams and uses artificial visionalgorithms. In addition, a case study is described in which the proposed technique is applied to guide an automated system through a maze drawn on a whiteboard by a user.
Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computervision, natural language processing, automated speec...
详细信息
Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computervision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.
暂无评论