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    1. UAV Path Prediction

      • Introduction

        • We utilize collected data from unmanned aerial vehicle (UAV) movements in the x, y, and z axes as input for model training. The Long Short-Term Memory (LSTM) architecture is employed to train the model, and the OSC AIMLFW framework is used as the training model framework. Through this process, a model capable of predicting the path of UAVs is trained, providing a solution for various use cases.

        • Ensure that the Influx database is operational for this use case, which is based on the OSC AI/ML Framework (Release-H).


      • Getting Started (Start from data insertion)

        • Step 1. Query influx token

          # Search the influx "token" value in below output
cat bitnami/influxdb/influxd.bolt | tr -cd "[:print:]"

        • Step 2. Create “UAVData” bucket (Inside Influx DB container)

          influx bucket create -n UAVData -o primary -t <token>

        • Step 3. Fill the file config (UAV_insert.py)

          DATASET_PATH = '/path/to/dataset.csv' # Replace to the UAV dataset path
INFLUX_IP = 'localhost'               # Influx IP 
INFLUX_TOKEN = 'VJpoNpqeVnjzvhpPm8jZ' # Influx token

        • Step 4. Excute the insert processing to insert data into Influx DB

          python3 insert.py
        • Step 5.  Upload UAV_pipeline.ipynb to aiml-notebook and generate a UAV_pipeline.yaml


        • Step 6. Start a UAV model training job


        • Step 7. Load the UAV model
          (To be added)


      • File List

        • UAV_dataset.csv (Download)
          The file contains collected UAV movement path data.

        • UAV_insert.py (Download)
          The file processes the UAV_dataset and inserts the data into InfluxDB.
          (Changed required: DATASET_PATH , INFLUX_IP , INFLUX_TOKEN)

        • UAV_pipeline.ipynb (Download)
          The file defines the model structure and training process.

        • UAV_input.json (Unfinished)
          The json file is used for sample data.

        • UAV_predict.sh (Unfinished)
          The script used for excuting the model prediction.

      • Example

        • Input:
          This input data represents a collection of points in a three-dimensional space, with each point defined by a set of three coordinates corresponding to the x, y, and z axes (After normalization).

        • Output:
          The output should be next xyz-axis path prediction (After normalization).

      • Contributor

        • Joseph Thaliath
        • Antony Wang
        • Jasmine Lee
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