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Autonomous Data Collection Using a Self-Organizing Map.

Jan Faigl, Geoffrey A Hollinger

    IEEE Transactions on Neural Networks and Learning Systems
    |April 4, 2017
    PubMed
    Summary
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    This study introduces a growing self-organizing map (SOM) for autonomous data collection, efficiently solving the traveling salesman problem (TSP) by adapting to sensor priorities and communication ranges.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • The traveling salesman problem (TSP) is a critical challenge in autonomous data collection, requiring efficient path planning for mobile agents to gather information from distributed sensors.
    • Existing heuristic algorithms for TSP can be computationally intensive and may not adapt well to dynamic environments with varying sensor priorities or communication constraints.

    Purpose of the Study:

    • To develop an adaptive and computationally efficient solution for autonomous data collection using a novel self-organizing map (SOM) approach.
    • To address the limitations of traditional TSP algorithms by incorporating a growing SOM that dynamically adjusts its structure.
    • To extend the proposed method for scenarios with prioritized sensors and varying communication radii, and for multi-vehicle coordination.

    Main Methods:

    Related Experiment Videos

    • Utilized a growing self-organizing map (SOM) that dynamically adapts the number of neurons during the learning process.
    • Applied the SOM to solve the traveling salesman problem (TSP) in the context of autonomous data collection from predeployed sensors.
    • Compared the performance of the proposed SOM approach against established combinatorial heuristic algorithms for TSP variants.

    Main Results:

    • The proposed growing SOM demonstrated improved results compared to existing combinatorial heuristic algorithms for relevant TSP variants.
    • The SOM-based approach proved to be less computationally demanding than traditional methods.
    • The learning procedure showed extensibility to scenarios with sensors having varying communication radii and to multi-vehicle planning.

    Conclusions:

    • The novel growing SOM offers an effective and efficient solution for autonomous data collection by addressing the complexities of the TSP.
    • The adaptive nature of the SOM allows for flexible data collection strategies, including sensor prioritization and handling of varied communication ranges.
    • The method provides a promising foundation for advanced multi-vehicle coordination and complex autonomous systems.