| Summary: | Deploying robots in unknown and complex areas for inspection tasks is becoming a real need for various application scenarios. Recently, there has been an increasing interest to develop and use autonomous aerial robots in environments such as urban voids and subterranean mine tunnels, aiming to decrease the human presence in dangerous or inaccessible areas. These areas are characterized by complete darkness and narrow tunnels, where the ground can often be rough and not traversible for mobile vehicles, thus the developments focus on Micro Aerial Vehicles (MAVs). MAVs are mechanically simple and agile platforms that can navigate through cluttered areas and have the potential to perform complex exploration tasks when equipped with proper onboard sensors. One of the key milestones in the development of autonomous robots is self-exploration. The definition of self-exploration according to [7] is "the act of moving through an unknown environment while building a map that can be used for subsequent navigation". By reaching this milestone, robots would be freed from the limitation of requiring already existing maps for navigation. In this thesis, a frontier-based exploration algorithm is established and evaluated to understand how such method could be used to reach the self-exploration milestone. By marking the border between what is known and unknown the method is able to determine the next desired position for the robot to expand the map. The resulting algorithm, together with a path planning method and 3-dimensional mapping framework, the method was tested and examined in simulated environments with different levels of complexity.
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