On the Impact of Uncertainty for Path Planning

Supplementary material, code and results for the ICRA 2019 paper by

Jérôme Guzzi, R. Omar Chavez-Garcia, Luca M. Gambardella, Alessandro Giusti, Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Lugano

Vist our webpage to know more about us!

Abstract

We consider the problem of planning paths on graphs with some edges whose traversability is uncertain; for each uncertain edge, \mdiff{we are given a probability of being traversable (e.g., by a learned classifier)}. We categorize different interpretations of the problem that are meaningful for mobile robots navigating partially-known environments, each of which yields a different formalization; we then focus on the case in which the true traversability of an edge is revealed only when the agent visits one of its endpoints (Canadian Traveller Problem). In this context, we design a large simulation campaign on synthetic and real-world maps to study the impact of two different factors: the planning strategy, and the amount of uncertainty (which could depend on the quality of the classifier producing traversability estimates).

Experimental results

You find all experimental results in folder results.

How to reproduce the experiments reported in the paper

The simplest way to reproduce the experiments is to use the provided docker image through docker-compose.

docker-compose pull
docker-compose up experiments

Running the whole experiments could take weeks depending on the number of cores.

To reduce the number of samples, use experiments/ral_mini.j2 or modify experiments/ral.j2, in particular set

• random_graph_number: The number of random graph to sample (for each experiment)
• real_map_classifier_samples: How many samples to draw for each classifier and each real map realization.

in

{% set random_graph_number = 100000 -%}
{% set real_map_classifier_samples = 100 -%}

To modify the number of assigned cores, set the pool parameter in docker-compose.yaml

    command:  python3 code/main.py --config experiments/ral.j2 --pool <NUMBER_OF_CORES>

Notebook

We provide a jupyter notebook to illustrate the experiments.

docker-compose up notebook

and open a browser window.

ICRA Poster

Take a look at the poster presented at the interactive session of ICRA 2019.