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Simulating GPS-Denied Autonomous UAV Navigation for Detection of Sur- Face Water Bodies This may be the author’s version of a work that was submitted/accepted for publication in the following source: Singh, Arnav Deo& Vanegas Alvarez, Fernando (2020) Simulating GPS-denied autonomous UAV navigation for detection of sur- face water bodies. In Proceedings of 2020 International Conference on Unmanned Aircraft Systems:ICUAS. Institute of Electrical and Electronics Engineers Inc., United States of America, pp. 1792-1800. This file was downloaded from: https://eprints.qut.edu.au/211603/ c IEEE 2020 This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. 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If there is any doubt, please refer to the published source. https://doi.org/10.1109/ICUAS48674.2020.9213927 Simulating GPS-denied Autonomous UAV Navigation for Detection of Surface Water Bodies Arnav Deo Singh Fernando Vanegas Alvarez Queensland University of Technology Queensland University of Technology Brisbane, Australia Brisbane, Australia [email protected] [email protected] Abstract— The aim to colonize extra-terrestrial planets has NASA has been developing a UAV for the exploration of been of great interest in recent years. For Mars, or other planets Mars and is aiming to launch it with their new rover in 2020 to sustain human life, it is essential that water is present and [2]. For the purposes of this research project, it will be accessible. Mars may contain or provide signs of the possibility assumed that this UAV is in the form of a quadcopter, is of near surface water, but this work will only focus on surface suitable for operating in the conditions of Mars, and has the water for the purposes of simulation. In this paper, we present same control dynamics as the ones used on Earth. a method for autonomous navigation and detection of surface water bodies in the GPS denied environments of Mars via a fully UAV and multispectral cameras have been previously autonomous UAV. A combination of existing state-of-the-art used in plant protection and farming for detecting specific tools and techniques have been utilized to enable the diseases or stress levels [3]–[6] and thus could be used in development of this system. Additionally, we create a modular missions for searching for water on Mars. framework to simulate the mission using the AirSim simulation environment and the Robot Operating System (ROS). The The research question for this project was to investigate simulation environment is leveraged by using the Unreal game how to develop a fully autonomous UAV to navigate the GPS engine running in Windows OS, interfacing with an open source denied environments of Mars, in the search for bodies of software for simultaneous localization and mapping (SLAM), surface water. This question can be divided into two sub- running on ROS and Linux OS. A simulated mission was questions as follows: successfully implemented and demonstrated using the framework. Results obtained indicate that the framework 1. What techniques will be used to develop a target enables the navigation of a UAV in the simulated Mars acquisition system capable of detecting bodies of environment and allows the UAV to detect surface water bodies. surface water? The developed simulation framework, along with the knowledge 2. What methodologies will be utilised to enable fully and techniques attained in this research, could accelerate the autonomous navigation in the GPS denied development, testing and deployment of missions for a real- environments of Mars? world Mars UAV, for the detection of surface water bodies. Additionally, this research aims to support and build upon prior Since it was not feasible to actually build and test this on work to further aid the search for water on other planets, as well Mars at the time of writing this paper, the development and as assisting humans in becoming a multi-planet species. testing was conducted using the AirSim simulation environment that was developed by Microsoft. AirSim is a Keywords—Mars UAV, surface water detection, UAV simulator and platform for experimenting with Artificial navigation in GPS denied environments, Mars exploration, space Intelligence (AI) techniques such as Deep Learning, exploration, SLAM, autonomous mission, simulation Computer Vision and Reinforcement Learning algorithms for autonomous vehicles. It supports Hardware-In-The-Loop I. INTRODUCTION (HITL) with supported devices and provides physically and The context of this research project and its application visually realistic simulations because it is developed as an relates to Target Acquisition missions on Mars; more Unreal Engine plugin [7]. specifically, the problem of searching for water on Mars via a A summary of the literature review on prior work will be fully autonomous UAV. Mars has been of great interest in the presented and will focus on the following: Aerospace industry in the last few years. As an example, private companies such as SpaceX are investing in • Detection of bodies of surface water – Mars may development and technology with their desire to colonize the have near surface water or provide signs of the red planet. If humans really were to colonize Mars, it would possibility of near surface water, but for the purposes be vital that a viable source of water is available. of developing this fully autonomous UAV in simulation, only surface water was explored. According to NASA [1], water is essential for life to exist; hence, a target acquisition system focused on detecting bodies o The types of sensors used for detection of surface water in a GPS-denied environment such the one on Mars is of great importance. 1 o Machine Learning techniques for water (�����−����) ����� = (1) detection (�����+����) o Modelling sensors for simulation Where Green and SWIR represent the spectral • Navigation in GPS denied environments since GPS reflectance measurements in the visible green band and is not available on Mars short-wave infrared band. If there is an absence of SWIR o Visual Odometry and SLAM techniques band in the multispectral image, the NDWI index can be used as it utilises the Near Infrared (NIR) band. An issue ▪ Feature based and direct SLAM with mNDWI however, is the fact that it cannot o Motion and mission planning using distinguish between water and snow. To fix this Partially Observable Markov Decision limitation, the mNDWI index should be used in Process (POMDP), since the objective of conjunction with a visible/NIR band [10]. the drone is to maximise its chance of locating a source of water while in a b) Machine Learning Techniques partially observable environment Machine Learning (ML) is the concept of enabling a o UAV motion control using AirSim computer to learn without being explicitly programmed to do so. Machine Learning can be divided into two categories; supervised learning (SL) and unsupervised II. LITERATURE REVIEW learning (UL). Supervised learning is when a machine is trained with labelled data, meaning that it learns from A. Target Acquisition data that is already identified. Once the machine has a) Detection of Surface Water Bodies trained with the labelled data, test data is fed into the Water on the surface of a planet is referred to as system and the algorithm produces a result based off the surface water; for instance, rivers, lakes and oceans knowledge it has acquired from training [12]. would be examples of surface water. There are two Unsupervised learning is when the machine is categories of sensors that can detect surface water: provided with unlabelled data, meaning that the data is microwave sensors and optical sensors. Microwave unidentified, and the unsupervised learning algorithm has sensors can operate under any weather condition and to identify trends or similarities in the data to produce an penetrate cloud and vegetation coverage due to its long outcome/result without any prior training. wavelength. Optical sensors (i.e. multispectral/ The task of detecting water from multispectral images hyperspectral) on the other hand, are widely used falls into the category of classification because it can because of the abundance of data and the appropriate either be detected as surface water or not surface water. spatial and temporal resolutions; that is, the number of Classification algorithms in SL can be used to enable the pixels utilised in construction of the image [8], and the machine to learn how to detect surface water from time duration for acquiring a single frame of a dynamic multispectral images; or, clustering algorithms in UL can process [9], [10]. also be used. A classification algorithm that has been used by [13] Since hyperspectral sensors have a larger number of and [14] to detect water from multispectral images is the bands that are narrow and provide more much more Support Vector Machine (SVM). When labelled training detail than multispectral sensors, it would be more suited data is provided to the SVM, an optimal hyperplane for applications that measure water quality rather than which classifies the data into two categories is detection of water [11].
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