Note: I am in the process of cleaning up the code, should be done by the end of sept 2020. Contact saroyam@oregonstate.edu if there is any problem with the code.
@InProceedings{saroya2020predictions,
author = {Manish Saroya and Graeme Best and Geoffrey A. Hollinger},
booktitle = {IEEE Int. Conf. Intelligent Robots and Systems (IROS)},
title = {Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions},
year = {2020},
}
Abstract Robotic exploration requires adaptively selecting navigation goals that result in the rapid discovery and mapping of an unknown world. In many real-world environments, subtle structural cues can provide insight about the unexplored world, which may be exploited by a decision maker to improve the speed of exploration. In sparse subterranean tunnel networks, these cues come in the form of topological features, such as loops or dead-ends, that are often common across similar envi- ronments. We propose a method for learning these topological features using techniques borrowed from topological image segmentation and image inpainting to learn from a database of worlds. These world predictions then inform a frontier-based exploration policy. Our simulated experiments with a set of real-world mine environments and a database of procedurally- generated artificial tunnel networks demonstrate a substantial increase in the rate of area explored compared to techniques that do not attempt to predict and exploit topological features of the unexplored world.
This repository is developed over an ongoing re-implementation of inpainting with partial-conv.
- Python 3.6+
- Pytorch 0.4.1+
pip install -r requirements.txt
Use synthetic data code to generate simulation data
Generate simulation data
python synthetic_data/map_generation.py
python synthetic_data/save_ground_persistence.py
python train.py
Network performance test
python test.py
Evaluate Exploration
python test/experiments.py
Example trajectories for the Edgar Experimental Mine when using our CNN-based prefiction method and the closest first method. Robot (blue) navigates from the start (red) at bottom centre. At each timestep, the robot decides which frontier (orange) to navigate to next.
Thanks to Abhijeet Agnihotri and Anna Nickelson for contributing to simulator and graph plots.