Wall-following Robot Navigation using Tensorflow DNN

This article aims to demonstratre how DNN can be used for wall-following Robot Navigation. The dataset is available on UCI Machine Learning repository.

Features are taken from 24 sensors readings and labels are Slight-Right-Turn, Move-Forward, Sharp-Right-Turn and Slight-Left-Turn.

We will try to use Tensorflow libraries as much as possible.

First, we download the dataset from the repository.

import tensorflow as tf
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00194/sensor_readings_24.data"
dataset = tf.keras.utils.get_file("sensor_readings_24.data",URL)

Then, we define the function that will be used to create the tuple (features, labels):

CSV_COLUMNS =  ['US1','US2','US3','US4','US5','US6','US7','US8','US9','US10','US11','US12','US13','US14','US15','US16','US17','US18','US19','US20','US21','US22','US23','US24', 'Class']
def train_input_fn():
  direction = tf.data.experimental.make_csv_dataset(
      dataset,
      batch_size=32,
      header=True,
      column_names=CSV_COLUMNS,
      label_name="Class")

  direction_batches = (
      direction.cache().repeat().prefetch(tf.data.AUTOTUNE))
  return direction_batches

Next step is to create the feature columns:

def create_feature_columns():
    """Creates feature columns from inputs.

    Returns:
        Dictionary of feature columns.
    """
    feature_columns = {
        colname : tf.feature_column.numeric_column(key=colname)
           for colname in ['US1','US2','US3','US4','US5','US6','US7','US8','US9','US10','US11','US12','US13','US14','US15','US16','US17','US18','US19','US20','US21','US22','US23','US24']
    }

    return feature_columns

fc = create_feature_columns()
for key,val in fc.items():
    exec(key + '=val')

Then, we build the model using DNNClassifier:

import tempfile
from tensorflow import keras

model_dir = tempfile.mkdtemp()
model = tf.estimator.DNNClassifier(
    model_dir=model_dir,
    hidden_units=[128,80,50,30,10],
    feature_columns=[US1,US2,US3,US4,US5,US6,US7,US8,US9,US10,US11,US12,US13,US14,US15,US16,US17,US18,US19,US20,US21,US22,US23,US24],
    n_classes=4,
    label_vocabulary=['Move-Forward','Slight-Right-Turn','Sharp-Right-Turn', 'Slight-Left-Turn'],
    activation_fn='relu',
    optimizer=keras.optimizers.Adam(learning_rate=0.005)
)

Now, time to proceed to the training and evaluate:

model = model.train(input_fn=train_input_fn, steps=12000)
result = model.evaluate(train_input_fn, steps=10)
for key, value in result.items():
  print(key, ":", value)

We can observe an accuracy of 95%.

Jupyter Notebook file is available on Github.

We will do another approach using Elman Network.