DDEnv

Automated Environment Reduction for Debugging Robotic Systems

Getting Started

Installation

Clone this repo:

$ git clone https://github.com/MissMeriel/DDEnv

Install ros dependencies:

sudo apt install python3-pip
sudo apt install ros-kinetic-robot-localization ros-kinetic-interactive-marker-twist-server ros-kinetic-controller-manager ros-kinetic-twist-mux ros-kinetic-move-base ros-kinetic-map-server ros-kinetic-move-base-msgs ros-kinetic-amcl ros-kinetic-joint-state-controller ros-kinetic-joint-state-publisher ros-kinetic-diff-drive-controller ros-kinetic-dwa-local-planner

sudo apt install shutter
sudo apt install libnet-dbus-glib-perl

Install sklearn for python3:

 pip3 install --upgrade pip
 pip3 install --upgrade setuptools
 pip3 install --upgrade scikit-learn

Usage

Several helper scripts are available to reduce runtime environment configuration. These helper scripts use the scenarios defined in the paper (published in ICRA 2021, preprint available here).

To recreate Table I of results in the paper, run:

$ cd world_parser/
$ ./results_runner.sh

This will kick off a series of subscripts to perform reduction for each environment using each schema for prioritization and partitioning. The output of each script will be printed to a log file in the results directory and track the reduction of the world at each invocation of the schema. The final results look like the following:

TEST METRICS:
Starting env size: 43
Minimal environment size:  2
Iterations to find minimal world:  17
Total number of worlds generated:  94
Total number of tests run:  35
Total number of reruns due to flakiness:  18
Total number of heterogeneous failures:  5
Total number of successful runs:  1

real	42m6.336s
user	2m51.120s
sys	0m52.033s

To recreate Figure 2 showing reduction over time of a scenario, run:

$ python gen_graph.py <logfile>

Acknowledgements

This material is based in part upon work supported by the National Science Foundation under grant numbers #1924777 and #1853374.