1. UAV Path Prediction (Guideline) - NTUST MITLab 

      • 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 UAV model


        • Step 8. Start model prediction
          1. obtain Ingress port for Kserve (kubectl get svc istio-ingressgateway -n istio-system)

          2. source uav.sh

      • 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_deploy.yaml (Download)
          The yaml file is used for deploying model inference service.

        • UAV_input.json (Download)
          The json file is the sample data for the prediction.

        • UAV_predict.sh (Download)
          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).

          • Example input

            UAV_input 
             [
      [0.7453425167510083, 0.057188434013895506, 0.3883151002000583],
      [0.9348491377517286, 0.5797269721343681, 0.15480809966429032],
      [0.32685562293198867, 0.40325520693639316, 0.37923173514277997],
      [0.07019999536045685, 0.26259026009845376, 0.2890220776419392],
      [0.562592860552039, 0.471677885159695, 0.41299704269747306],
      [0.8156868208972661, 0.6577897226335571, 0.5894501267006358],
      [0.6789936054809089, 0.5219624142693665, 0.4196227577850472],
      [0.10702478626584111, 0.3030673792265356, 0.5265722005767963],
      [0.9762029048313448, 0.30233720269747666, 0.4147831180404825],
      [0.5277344636914802, 0.1058208829010896, 0.9369119762104259]
    ]

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

          • Example output

            UAV_output 
            Inference Result: [[-0.07196578  0.05871227 -0.00614708]]

      • Contributor

        • Joseph Thaliath
        • Antony Wang
        • Jasmine Lee
  • No labels