UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environme...
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ftdoajarticles:oai:doaj.org/article:5767b601537646d3bd089aa68a44a93e 2023-05-15T14:02:19+02:00 UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration Julian Galvez-Serna Fernando Vanegas Shahzad Brar Juan Sandino David Flannery Felipe Gonzalez 2022-12-01T00:00:00Z https://doi.org/10.3390/drones6120391 https://doaj.org/article/5767b601537646d3bd089aa68a44a93e EN eng MDPI AG https://www.mdpi.com/2504-446X/6/12/391 https://doaj.org/toc/2504-446X doi:10.3390/drones6120391 2504-446X https://doaj.org/article/5767b601537646d3bd089aa68a44a93e Drones, Vol 6, Iss 391, p 391 (2022) autonomous mission planning planetary exploration unmanned aerial vehicle (UAV) partially observable Markov decision process (POMDP) reinforcement learning (RL) robot operating system (ROS) Motor vehicles. Aeronautics. Astronautics TL1-4050 article 2022 ftdoajarticles https://doi.org/10.3390/drones6120391 2022-12-30T19:32:15Z Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration. Article in Journal/Newspaper Antarc* Antarctica Directory of Open Access Journals: DOAJ Articles Titan ENVELOPE(-68.733,-68.733,-72.083,-72.083) Drones 6 12 391 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
autonomous mission planning planetary exploration unmanned aerial vehicle (UAV) partially observable Markov decision process (POMDP) reinforcement learning (RL) robot operating system (ROS) Motor vehicles. Aeronautics. Astronautics TL1-4050 |
spellingShingle |
autonomous mission planning planetary exploration unmanned aerial vehicle (UAV) partially observable Markov decision process (POMDP) reinforcement learning (RL) robot operating system (ROS) Motor vehicles. Aeronautics. Astronautics TL1-4050 Julian Galvez-Serna Fernando Vanegas Shahzad Brar Juan Sandino David Flannery Felipe Gonzalez UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
topic_facet |
autonomous mission planning planetary exploration unmanned aerial vehicle (UAV) partially observable Markov decision process (POMDP) reinforcement learning (RL) robot operating system (ROS) Motor vehicles. Aeronautics. Astronautics TL1-4050 |
description |
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration. |
format |
Article in Journal/Newspaper |
author |
Julian Galvez-Serna Fernando Vanegas Shahzad Brar Juan Sandino David Flannery Felipe Gonzalez |
author_facet |
Julian Galvez-Serna Fernando Vanegas Shahzad Brar Juan Sandino David Flannery Felipe Gonzalez |
author_sort |
Julian Galvez-Serna |
title |
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
title_short |
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
title_full |
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
title_fullStr |
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
title_full_unstemmed |
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration |
title_sort |
uav4pe: an open-source framework to plan uav autonomous missions for planetary exploration |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/drones6120391 https://doaj.org/article/5767b601537646d3bd089aa68a44a93e |
long_lat |
ENVELOPE(-68.733,-68.733,-72.083,-72.083) |
geographic |
Titan |
geographic_facet |
Titan |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_source |
Drones, Vol 6, Iss 391, p 391 (2022) |
op_relation |
https://www.mdpi.com/2504-446X/6/12/391 https://doaj.org/toc/2504-446X doi:10.3390/drones6120391 2504-446X https://doaj.org/article/5767b601537646d3bd089aa68a44a93e |
op_doi |
https://doi.org/10.3390/drones6120391 |
container_title |
Drones |
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6 |
container_issue |
12 |
container_start_page |
391 |
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1766272542586175488 |