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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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A Survey on Learning Motion Planning and Control for Mobile Robots: Toward Embodied Intelligence.

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    This survey classifies learning-based motion planning for mobile robots to achieve embodied artificial intelligence (AI). It reviews machine learning approaches, system architectures, and future research directions for embodied AI.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Mobile robots are crucial for environmental monitoring and search-and-rescue.
    • Embodied artificial intelligence (AI) enables robots to learn and evolve through environmental interaction.
    • Motion planning and control are essential for effective robot operation.

    Purpose of the Study:

    • To provide a comprehensive overview and classification of learning-based motion planning and control for mobile robots.
    • To categorize existing approaches based on system architectures and machine learning algorithms.
    • To highlight key technologies and challenges in achieving embodied AI.

    Main Methods:

    • Categorization of approaches into five system architectures based on ML module application.
    • Review of reinforcement learning (RL), imitation learning, and ML-MPC integration.
    • Presentation of research on safe learning control, Sim2Real transfer, and large language models (LLMs).

    Main Results:

    • A taxonomy of learning-based motion planning from a system architecture perspective.
    • Detailed review of ML techniques applied across different architectures.
    • Identification of critical issues and challenges in embodied AI.

    Conclusions:

    • The survey offers a novel system architecture-based classification of embodied AI research in mobile robotics.
    • It emphasizes technologies crucial for real-world applications.
    • The work aims to advance embodied AI in mobile robots through future research directions.