https://github.com/autonomous-drone-racing-lab/efficient-path-planner
https://github.com/autonomous-drone-racing-lab/efficient-path-planner
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Autonomous-Drone-Racing-Lab
- Language: C++
- Default Branch: main
- Size: 8.96 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Efficient-Path-Planner
Installation
Install Dependencies
To use this code, relevant other packages must be installed before. Below you will find a list of instructions
Install Submodules
One external dependency (Pybind11) must be added to this codebase in the form of a Git Submodule. (The other external dependencies for path planning are already provided within this repository). To install the submodule run
git submodule init
git submodule update --remote
If you have not cloned this repo yet, you can also download all submodules during the clone via
git clone --recurse-submodules https://github.com/Autonomous-Drone-Racing-Lab/Efficient-Path-Planner
Install Eigen
sudo apt install libeigen3-dev
Install glog
From outside this directory e.g. code directory, run
```
Fetch glog in version 6
git clone https://github.com/google/glog.git --branch v0.6.0 cd glog
cmake -S . -B build -G "Unix Makefiles" cmake --build build cmake --build build --target install ```
Install YAML CPP
sudo apt-get install libyaml-cpp-dev
Install OMPL
Follow the tutorial on the ompl website
Build Code
To generate the python binding simply run
pip install .
from the root of this package. This makes the path planning package available to python via the name polynomial_trajectory, i.e. import polynomial_trajectory
Important: In case the first run fails, you must first setup the build system. For this follow the steps:
mkdir build
cd build
cmake ..
As build errors are obfuscated in the pip install . command for debugging purposes it is recommended to do a normal C-Make build for debugging
cd build
make
Third Party Software
Within our work, we utilized different software of other people. Important to mention are - Mav Trajectory Generation for experimenting with minimum snap trajectories - Tobias Kunz for providing implementations of his time-parametrization algorithm
Owner
- Name: Autonomous Drone Racing Lab
- Login: Autonomous-Drone-Racing-Lab
- Kind: organization
- Repositories: 1
- Profile: https://github.com/Autonomous-Drone-Racing-Lab
GitHub Events
Total
Last Year
Dependencies
- quay.io/pypa/manylinux1_x86_64 latest build
- pytest >=6.0