https://github.com/winstxnhdw/offlinepathoptimisation
An investigation of various path planning optimisation techniques from academic papers in Jupyter Notebook.
https://github.com/winstxnhdw/offlinepathoptimisation
constraints minimize optimisation path-planning path-smoothing scientific-computing scipy visualisation waypoints
Last synced: about 2 months ago
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An investigation of various path planning optimisation techniques from academic papers in Jupyter Notebook.
- Host: GitHub
- URL: https://github.com/winstxnhdw/offlinepathoptimisation
- Owner: winstxnhdw
- Created: 2021-04-06T18:42:24.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-08T14:19:59.000Z (over 2 years ago)
- Last Synced: 2025-03-31T02:34:57.608Z (3 months ago)
- Topics: constraints, minimize, optimisation, path-planning, path-smoothing, scientific-computing, scipy, visualisation, waypoints
- Language: Jupyter Notebook
- Homepage:
- Size: 135 MB
- Stars: 9
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# OFFLINE PATH PLANNING OPTIMISATION
This notebook elaborates the testing and development of an offline path planning optimisation pipeline to generate a safe and feasible reference path for an ego vehicle.
## Objective
The pipeline takes a set of coarsely placed waypoints and adjusts their localisation so as to comply with certain **path curvature** constraints in view of the vehicle's size and steering capability. The adjusted waypoints should be as close as possible to the original waypoints without violating any curvature constraints.
## Approaches
- [Focused Trajectory Planning for Autonomous On-Road Driving](https://www.ri.cmu.edu/pub_files/2013/6/IV2013-Tianyu.pdf) by Gu et. al.
- [Segmented Constrained Path Optimisation](examples/constrained.ipynb)
- [Unconstrained Waypoint Optimisation](examples/unconstrained.ipynb)
- [Controlled Unconstrained Waypoint Optimisation](examples/controlled.ipynb)
## Results
A controlled real-world test was conducted only on the waypoints generated from the [unconstrained approach](examples/unconstrained.ipynb). Empirical data indicated higher comfort levels during transportation when compared to the tracking of unoptimised waypoints. With curb information accounted for ([see the controlled approach](examples/controlled.ipynb)), the resultant path held a safer distance from the curb without any noticeable loss in passenger comfort.
## Requirements
```bash
pip install -r requirements.txt
```