Galvez, Julian (Queensland University of Technology), Gonzalez, Luis Felipe (Queensland University of Technology (QUT)/ Australian Research C), Vanegas Alvarez, Fernando (Queensland University of Technology), Flannery, David Timothy (Queensland University of Technology)

A Probabilistic Based UAV Mission Planning and Navigation for Planetary Exploration

Scheduled for presentation during the Poster Session "Poster Papers Session" (WePS), Wednesday, September 2, 2020, 13:00−18:00, Foyer, Mezzanine Level

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on April 18, 2024

Abstract

The use of Unmanned Aerial Vehicles (UAVs) for Search And Rescue (SAR), powerlines, air quality and other applications is increasing. Their use has also been considered for planetary exploration (e.g. Mars, Titan). One exciting development in UAVs is a test planned by NASA of an unmanned helicopter in the atmosphere of Mars; this aims to establish a new dimension and direction for the planetary exploration field. Future missions will require advanced navigation tools supporting mission planning. The autonomy of UAVs systems will continue to grow for Earth applications supported by mathematical tools, models and formulations that help the UAV to deal with critical aspects of the mission. Planetary exploration is challenging and is influenced by different levels of uncertainty in UAV localization and the environment itself. Probabilistic navigation allows planning with uncertainty. This paper presents a high-level mission planning and navigation architecture for planetary exploration based on Partially Observable Markov Decision Process (POMDP). We focus on planetary exploration missions for biosignature detection. The paper presents a mission planning architecture and describes the results of a POMDP-based navigation and target finding module emulating biosignatures with ArUco markers in a Mars simulated environment.