Frontier-based Autonomous 3D : Exploration for UAVs
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Autonomous exploration is a crucial task that enables a mobile robot to navigate
through unfamiliar environments while simultaneously mapping the surroundings.
By acquiring a comprehensive understanding of the environment, the vehicle can
navigate safely and efficiently, determining the shortest obstacle-free paths to
reach any location within the map. Exploration forms a fundamental component
of various mobile robotic applications, including scene reconstruction, search and
rescue operations, and continuous monitoring. Initially, autonomous exploration
was primarily implemented using Unmanned Ground Vehicles (UGVs), which
offered robustness and extended autonomy. However, UGVs were limited to op-
erating in a 2D space, restricting coverage range and the amount of information gathered. With the advancements in Unmanned Aerial Vehicles (UAVs) and
their increased autonomous capabilities, along with reduced manufacturing costs,
UAVs have emerged as a promising solution to overcome these limitations and
significantly expand the boundaries of robotic exploration due to their flexibility
and expanded coverage range.
Nevertheless, implementing autonomous exploration with UAVs introduces
additional complexity due to the three-dimensional (3D) operating space.
gathered. With the advancements in Unmanned Aerial Vehicles (UAVs) and
their increased autonomous capabilities, along with reduced manufacturing costs,
UAVs have emerged as a promising solution to overcome these In this context, this thesis project aims to design and implement an explo- ration algorithm that enables a UAV to autonomously navigate unexplored en- vironments. This is accomplished by establishing an exploration strategy that not only ensures a fast exploration but also provides a meticulous analysis of the environment, thereby reducing the risk of local minima. The proposed approach uses a LiDAR sensor as the primary source of information. This data informs a frontier-based exploration technique [16] integrated within the OctoMap Frame- work [9], facilitating efficient and rapid map analysis. The proposed approach strives to strike a balance between the exploration of the unknown and the exploitation of known map data. The exploitation compo- nent, driven by a greedy behavior, seeks to minimize travel distance by targeting the nearest exploration point. Conversely, exploration is facilitated by storing all detected exploration targets throughout the process.