Skip to content

Latest commit

 

History

History
65 lines (55 loc) · 1.58 KB

README.md

File metadata and controls

65 lines (55 loc) · 1.58 KB
Raw

planning-algorithms

Experimental implementations of Planning Algorithms by Steven LaValle.

Installation

git clone [email protected]:mech0ctopus/planning-algorithms.git

## C++
cd planning-algorithms/modules/cpp
mkdir build && cd build
cmake ..
make

## Python
cd planning-algorithms/modules/python3
# Install in editable mode (don't forget the period at the end)
pip3 install -e .

Tests

## Run all tests
cd planning-algorithms
./run_tests.sh

## C++
cd planning-algorithms/modules/cpp/build
./TestPrimitives

## Python
cd planning-algorithms/modules/python3
nosetests3 .

Examples

cd planning-algorithms/modules/python3/examples
python grid2d.py
python grid3d.py
python five_state.py
python optimal_grid2d.py
python optimal_grid3d.py
python optimal_five_state.py

TODO

  • Update unit tests for ForwardLabelCorrecting

TODO Later

  • Optimal Planning with Forward Value Iteration

Maybe Later

  • Implement:
    • A-Star
    • IterativeDeepening
  • Define a better SearchProblem interface
  • Mark Initial/Goal States as VISITED in bidirectional search
  • Create/Implement more examples
  • Determine a path forward for C++ work
  • Identify a better design pattern to use for constructing Forward/Backward versions of the same search algo. (Factory?)
    • Should Forward, Backward, and Bidirectional search be consolidated into a Unified interface?
  • Add plot results to problem class
  • Raise exception if search fails (instead of Return code)
  • Add logic to ForwardSearch for checking if a state is alive or dead per p.33