This tool allows users to test autonomous drone navigation policies in simulation, based on real-world drone missions. See getting_started.ipynb for detailed instructions on the provided scripts.

Funding Sources

This work was supported by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Additional support was also provided by the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), which is funded by the US National Science Foundation under Award #2112606. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Developed for the paper: A Framework for Autonomic Computing for In Situ Imageomics. If you use this code, please consider citing it.

@INPROCEEDINGS{10336017,
  author={Kline, Jenna and Stewart, Christopher and Berger-Wolf, Tanya and Ramirez, Michelle and Stevens, Samuel and Babu, Reshma Ramesh and Banerji, Namrata and Sheets, Alec and Balasubramaniam, Sowbaranika and Campolongo, Elizabeth and Thompson, Matthew and Stewart, Charles V. and Kholiavchenko, Maksim and Rubenstein, Daniel I. and Van Tiel, Nina and Miliko, Jackson},
  booktitle={2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)}, 
  title={A Framework for Autonomic Computing for In Situ Imageomics}, 
  year={2023},
  pages={11-16},
  keywords={Social groups;Ecosystems;Wildlife;Machine learning;Data collection;Cameras;Object recognition;autonomous flight;UAVs;ecology;machine learning;computer vision;imageomics},
  doi={10.1109/ACSOS58161.2023.00018}}