https://github.com/colasgael/genetic-algorithm-for-offline-path-planning

Genetic Algorithm for Offline Path Planning in 2D maps.
https://github.com/colasgael/genetic-algorithm-for-offline-path-planning

genetic-algorithm optimization path-planning python

Last synced: 5 days ago
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Genetic Algorithm for Offline Path Planning in 2D maps.

• Host: GitHub
• URL: https://github.com/colasgael/genetic-algorithm-for-offline-path-planning
• Owner: ColasGael
• License: mit
• Created: 2019-06-11T21:19:26.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-06-11T21:35:52.000Z (over 5 years ago)
• Last Synced: 2024-06-11T17:39:52.770Z (5 months ago)
• Topics: genetic-algorithm, optimization, path-planning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.33 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Genetic-Algorithm-for-Offline-Path-Planning

by Gael Colas and Ianis Bougdal-Lambert, graduate students from Stanford University.

This repository gathers our final project for the AA222: "Engineering Design Optimization" class at Stanford (2018). Our teacher was Pr. Mykel Kochenderfer.

Language: Python

Goal: Study the use of a genetic algorithm (GA) for the problem of

offline path planning on a 2D map. 

The problem can be framed as follow: find the optimal feasible path to move from a starting position to a target location across a flat map of

a terrain with impenetrable obstacles. 

A scripts generate maps with variable difficulties. The feasible paths must be evaluated according to two criteria: their length and their difficulty. 

In a first approach, only the length of the paths was considered in a Single-Objective minimization process. 

Then the initial algorithm was extended to optimize under the two criteria (MOGA), in a Pareto optimality search. 

Finally, the performance of the algorithms was compared to the performance of state-of-the-art Optimal Control tools.

More details about the project, the data and the model can be found by reading the report and the presentation located at the root: "aa222_final-report_team12-report.pdf" and "aa222_final-presentation_team12.pdf".