Path Optimisation Project

Overview

This project aims to optimize the construction of a beginner's trail in a mountainous region of the Free State, minimizing energy expenditure for participants. The solution includes ingestion of altitude map and energy expenditure data, modeling with machine learning techniques, A* search algorithm for path optimisation, and reporting the optimal path.

Table of Contents

• Requirements
• Usage
• Data Files
• Modelling and Optimisation
• Optimisation
• Results
• Future Improvements

Requirements

In order to run the solution, you must know that the code was created and ran in Python 3.10.9 version. Also, the packages used in the code are:

• numpy==1.23.5
• pandas==1.5.3
• matplotlib==3.7.0
• sckikit-learn==1.2.2

Usage

The code to obtain the optimal path is: path-optimisation-script-solution.py

Data Files

The available data for this problem is the following:

• energy_cost.csv
  • This table contains the measurements from a sports science lab that relates the walking gradient/slope on a treadmill with the energy expended of multiple test subjects.

    Energy expenditure is measured in $J.kg^{-1}.min^{-1}$, calculated based on oxygen consumption and treadmill speed

• altitude_map.csv
  • This table contains information that describes an altitud map of the region of interest.

    The altitude map is at resolution of 10m x 10m, measurements are in meters, with North and the Y-axis going up vertically

For more details of the process, see the path-optimisation-nb-solution.ipynb file in the repository.

Modeling and Optimisation

For more details of the process, see the path-optimisation-nb-solution.ipynb file in the repository.

Results

For more details, see the optimal_path_coords.csv and optimal_path_visualization.png files in the repository.

Future Improvements

For more details, see the optimal_path_advice.txt file in the repository.