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Dynoplan 🦖

Note: We have just submitted a publication to T-RO. Preprint is available here. We will clean the code, update the readme and write a small tutorial in the following weeks.

Dynoplan is a small library for solving kinodynamic motion planning problems, as defined in Dynobench 🦖. It implements 3 different algorithms: Trajectory Optimization with geometric initial guess (RRT*-TO), Sample based Motion Planning (SST*), and Iterative Search and Optimization (iDb-A*).

The first version kinodynamic-motion-planning-benchmark is now deprecated.

Robots and Problem Description

Kinodynamic motion planning problem are defined in Dynobench

Examples and Tests

Check the tests in test to learn how to use the code!

Planners

  • Trajectory Optimization: several algorithms for optimization with free terminal time, built on top of Differential Dynamic Programming (Crocoddyl).
  • RRT*-TO: Geometric Planner RRT* (OMPL) + Trajectory Optimzation
  • iDb-A*: Iterative disccontinuity bounded search and Trajectory Optimization
  • SST*: Stable Sparse Tree (OMPL)
  • Dbrrt, AO-dbrrt and DBrrtConnect, DB-SST* (coming soon!)

Building

You can check the Github CI cmake.yml to see how to compile the project in latest ubuntu (For ubuntu 20.04, we experienced some issues with g++-9, but clang-13 is fine.)

Dependencies:

  • Boost
  • fcl (0.7)
  • yaml-cpp
  • Eigen3
  • Crocoddyl (1.8)
  • OMPL (1.6)

We need OMPL 1.6 for planners RRT + TO and SST. We recommend to install OMPL in a local directory with -DCMAKE_INSTALL_PREFIX, and use -DCMAKE_PREFIX_PATH here

Motion Primitives

You will find a small set of motion primitives for each system in dynobench. 5000 primitives per system are available in dynomotions. These primitives are required for running the test.

Finally, the comple set of primitives for each system can be downloaded from Google Drive. This can be done manually with a web browser or using the command line with gdown. This is required to run the benchmark. For example:

gdown --fuzzy "https://drive.google.com/file/d/1r_ecGwdfvWnVWxPsvR4d8Hjcayxg5PsB/view?usp=drive_link"

We provide the script download_primitives.bash to download all primitives, that will be stored in folder dynomotions_full.

All primitive in two ZIP files: https://drive.google.com/drive/folders/1-Nvctva17I8aFsWvHfdQFWTIDUNWwgcM?usp=drive_link

Primitves per system:

How to generate motion primitives for new systems

We will show how to generate motion primitives for the integrator1_2d_v0

  • Step one: Implement the Dynamics in Dynobench, following the tutorial for the Integrator2_2d in the README (in this case integrator1_2d_v0 is already implemented)

  • Step two: Solve Optimization Problems with Random Start and Goals

./main_primitives --mode_gen_id 0  --dynamics integrator1_2d_v0 --models_base_path ../dynobench/models/   --max_num_primitives 200 --out_file /tmp/my_motions.bin

Primitives will be store in /tmp/my_motions.bin and /tmp/my_motions.bin.yaml. You can pass options to the solver for trajectory optimization.

  • Step Three: Improve the cost of the primitives
./main_primitives --mode_gen_id 1  --dynamics integrator1_2d_v0 --models_base_path ../dynobench/models/   --max_num_primitives 200  --in_file /tmp/my_motions.bin --solver_id 1

By default, primitives are stored in /tmp/my_motions.bin.im.bin and /tmp/my_motions.bin.im.bin.yaml. You can pass options to the solver for trajectory optimization.

  • Step Fours: Randomnly cut primitives
./main_primitives --mode_gen_id 2 --in_file     /tmp/my_motions.bin.im.bin    --max_num_primitives -1   --max_splits 1  --max_length_cut 50  --min_length_cut 5 --dynamics integrator1_2d_v0 --models_base_path ../dynobench/models/

By default, primitives will be stored in /tmp/my_motions.bin.im.bin.sp.bin and /tmp/my_motions.bin.im.bin.sp.bin.yaml

Done!

Additionally, main_primitives provide more useful functionality, such as conversion between formats, computing statistics, generating primitives with random rollouts, sorting primitives and resampling of primitives.

Benchmark

Results of reported in our TRO paper are in folder tro_results. To replicate the results use commit: xxxxx. The code is under continuous development, but the benchmark should work also with newer commits. If you experience any problem, please open an ISSUE.

First, download primitives with:

bash -x download_primitives.bash

Primitvies are stored in a new dynomotions_full directory. Next, move to the build directory and run commands:

Benchmark between planners

python3 ../benchmark/benchmark.py -m bench -bc    ../benchmark/config/compare.yaml

Generate fancy table

python3  ../benchmark/benchmark.py --mode fancy --bench_cfg ../benchmark/config/plot_results_v3.yaml

(last version)

python3  ../benchmark/benchmark.py --mode fancy --bench_cfg ../benchmark/config/plot_results_v4.yaml

Generate table for website

python3  ../benchmark/benchmark.py --mode fancy --bench_cfg ../benchmark/config/plot_results_v_all.yaml

(last version)

python3  ../benchmark/benchmark.py --mode fancy --bench_cfg ../benchmark/config/plot_results_v_all_v4.yaml

Generate plots only for three for the paper.

PAPER=1 python3  ../benchmark/benchmark.py --mode bench --bench_cfg  ../benchmark/config/plot_results_v3_threePlots.yaml

Search heuristic

Study of heuristic functions

python3 ../benchmark/benchmark.py -m bench_search -bc    ../benchmark/config/bench_search.yaml

Only analyze the results

python3 ../benchmark/benchmark.py -m bench_search -bc    ../benchmark/config/bench_search_make_table_tro.yaml

Results for website

python3 ../benchmark/benchmark.py -m bench_search -bc    ../benchmark/config/bench_search_make_table_tro_all.yaml

Optimization with Free terminal time

Study of strategy for trajectoy optimization with free terminal time

python3 ../benchmark/benchmark.py -m bench_time -bc    ../benchmark/config/bench_time.yaml

Latex Table with subset of results shown in the paper:

time python3 ../benchmark/benchmark.py -m bench_time -bc ../benchmark/config/make_table_time_tro.yaml

For website table:

time python3 ../benchmark/benchmark.py -m bench_time -bc ../benchmark/config/make_table_time_tro_all.yaml

Study of time spent in each component

python3   ../benchmark/benchmark.py -m study  -bc ../benchmark/config/bench_abblation_study.yaml

(Results in TRO)

python3   ../benchmark/benchmark.py -m study  -bc ../benchmark/config/bench_abblation_results_tro.yaml

You can modify each config file to change the number of runs, the evaluated problems and the maximum time. The configurations files we used for TRO have prefix TRO.

The paramteres for each algorithm are in .yaml files inside the benchmark/config/algs directory, for example idbastar_v0.yaml.

Visualization

Plot environments (modify python file to select which envs to print)

cd build
python3 ../plot_all_envs.py

Visualize trajectores

DO something

Citing

If you use or work for academic research, please cite:

@misc{ortizharo2023idba,
      title={iDb-A*: Iterative Search and Optimization for Optimal Kinodynamic Motion Planning},
      author={Joaquim Ortiz-Haro and Wolfgang Hoenig and Valentin N. Hartmann and Marc Toussaint},
      year={2023},
      eprint={2311.03553},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

@misc{hoenigDbADiscontinuityboundedSearch2022,
  title = {Db-A*: Discontinuity-Bounded Search for Kinodynamic Mobile Robot Motion Planning},
  author = {Hoenig, Wolfgang and Ortiz-Haro, Joaquim and Toussaint, Marc},
  year = {2022},
  eprint = {2203.11108},
  eprinttype = {arxiv},
  url = {http://arxiv.org/abs/2203.11108},
  archiveprefix = {arXiv}
}

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