ROS Interface for ProSeCo Planning - Probabilistic Semantic Cooperative Planning

Table of Contents

• ProSeCo Planning - Probabilistic Semantic Cooperative Planning
  • Overview
  • Setup
  • Run Instructions
    • Evaluate
    • Analyze
    • Optimize
  • Configuration Instructions
    • Options
    • Scenarios
    • Evaluator
  • Formatting
  • Documentation
  • Profiling
  • Citation

ProSeCo Planning C++ library

The ProSeCo Planning C++ library can be found here.

Overview

An introduction to the project can be found here

├── README.md                                # This file
├── CMakeLists.txt                           # Project level CMake file
├── doc                                      # Documentation
├── config                                   # Configuration files
│   ├── evaluator                            # Configuration of the evaluator
│   ├── hyperparameter_optimization          # Configuration of the hyperparameter optimization
│   ├── options                              # Configuration of the planning algorithm
│   └── scenarios                            # Configuration of the different scenarios
├── python
|    └── proseco                              # ProSeCo Planning Python package
|        ├── evaluator                        # Module for the evaluation of the algorithm
|        ├── dashboard                        # Module for the visualization of the evaluator results
|        ├── hyperparameter_optimization      # Module for the optimization of the hyperparameters
|        ├── testing                          # End-to-end tests for the ProSeCo Planning C++ library
|        ├── tests                            # Unit tests for the ProSeCo Planning Python package
|        ├── utility                          # Module with utility functions
|        └── visualization                    # Module with visualization functions
├── include
│   └── ros_proseco_planning                  # Header files
└── src                                       # Source files

Setup

Please follow the instructions in the ProSeCo Planning workspace to get started with the library.

Run Instructions

1. Source the environment: . proseco_ws/devel_isolated/setup.bash
2. Activate the Python virtual environment
3. Start a ROS core: roscore

Evaluate

cd python/proseco && python evaluator/evaluator.py -c config.json -y -s

Analyze

cd python/proseco && python dashboard/index.py

Optimize

cd python/proseco && python hyperparameter_optimization/optimize.py -f optimizer -c config.json

Configuration Instructions

The behavior of the algorithm can be configured entirely by changing the CUE/JSON based configuration files.

Options

The options determine the algorithm's configuration.

Scenarios

The scenarios describe different traffic scenarios with which the algorithm can be evaluated.

Evaluator

The evaluation configuration determines the evaluation that is being performed.

Formatting

The ProSeCo Planning workspace provides a script to format all .cpp, .h and .py files.

C++

All .cpp and .h files must be formatted using clang-format.

Python

All .py files must be formatted using black.

JSON and HTML

All .json and .html files must be formatted using Prettier.

CUE

All .cue files must be formatted using cue, e.g. cue fmt <file_name>.

Documentation

C++ Documentation

The documentation for the ROS package can be found here.

The documentation can be generated using doxygen. cd doc && doxygen Doxyfile

Python Documentation

The documentation can be generated using sphinx.js
cd doc && python generate_docs.py

Profiling

The resulting binary can be profiled using:

1. valgrind --tool=callgrind --callgrind-out-file=callgrind.out --instr-atstart=no ./proseco_planning_node ros_proseco_planning_node example_options.json sc01.json
2. kcachegrind callgrind.out

Citation

Please refer to the respective publication if you are using it for your work. Thank you very much 🙂!