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This repository contains Python code for the paper "Autonomous Navigation in Unknown Environments with Sparse Kernel-based Occupancy Mapping"

SCOPE

For simplicity, the code is for a 10x10 simulated environment instead of the ROS environment used in the paper. However, the main algorithms are the same.

DEPENDENCIES

The code depends on the following software and packages: python3.x, numpy, matplotlib, queue, rtree

COMMAND TO RUN THE CODE

python main.py

FILES

Important code

  1. main.py: code for autonomous mapping and navigation algorithm
  2. perceptron.py: code related to main contributions: Fastron and collision checking algorithms.
  3. robot.py: code for collecting observations and retraining kernel perceptron

Others

  1. env.py: code to generate a simulated environment
  2. kernels.py: code for multiple kernel functions.
  3. astar.py: code for A*
  4. viz.py: code for visualization

VISUALIZATION EXPLAINED

  1. Start cell: top left corner.
  2. Goal cell: bottom right corner.
  3. Green triangle: robot.
  4. Blue boundary: decision boundary by Fastron score.
  5. Magenta dashed boundary: inflated boundary by the upper bound.
  6. Blue arrows: A* path.
  7. Yellow arrows: traveled path.

TEST

The code has been tested on Ubuntu 16.04 and Python 3.6.