An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environments

Abstract

This paper introduces an autonomous system employing multirotor unmanned aerial vehicles for fast 3D exploration and inspection of vast, unknown, dynamic, and complex environments containing large open spaces as well as narrow passages.The system exploits the advantage of small-size aerial vehicles capable of carrying all necessary sensors and computational power while providing full autonomy and mobility in constrained unknown environments. Particular emphasis is put on the robustness of the algorithms with respect to challenging real-world conditions and the real-time performance of all algorithms that enable fast reactions to changes in environment and thus also provide effective use of limited flight time. The system F I G U R E 1 The proposed system operating in a simulated disastrous scenario during the Urban Circuit of the Subterranean Challenge. The figure shows the unmanned aerial vehicle platform as a part of a multirobot team deployed within open spaces (a) and confined corridors (b) of the unfinished nuclear power plant in the

COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations

“…On the topic of very agile motion the related work [43] shows experimental results for up to 1.8m/s average exploration speed as compared to the 2.3m/s using COMPRA in a similar tunnel environment. High-speed navigation in narrow or constrained subterranean environments is a very difficult problem, and other related works are evaluated (in real-life constrained environments) up to: 0.1m/s [19], 0.4 − 0.5m/s [13], 0.5m/s [16], 0.5m/s [18], 0.75m/s [26], as compared to the 0.9 − 1m/s full mission speed and the 2.3m/s navigation evaluation speed of COMPRA. Additionally, COMPRA Fig.…”

“…In [13] a framework for fast exploration in unknown 3D environments has been proposed, incorporating localization, mapping, planning, exploration and object detection and localization sub-components. The main focus of the system was on a grid-based path planner, enhanced with path post-processing, while the exploration behaviour was designed using a frontier-based strategy.…”

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots

“…While the vast majority of studies on aerial robot exploration have focused solely on optimizing the primary exploration behavior, there has been a few recent studies that propose approaches to full-mission autonomy with finite state machines [14,16,17,25] and behavior trees [23,26]. We are particularly interested in behavior trees [10], which are common in computer games and have recently gained popularity in robotics [27,29,32], due to the ease of design and introspection.…”

“…Exploration algorithms are loosely characterized as information theoretic [6,11,19,34], which are seen as mathematically sound, frontier-based [7,17,20,28,37,40], which are computationally efficient, or graph search-based [8,12,13,38], which are scalable. Our pipeline is primarily frontier-based, but with a new frontier definition and selection heuristic, and also uses graph search for specific behaviors.…”

“…Aerial exploration strategies are typically combined with local path planners, which enable faster and safer navigation. Common approaches include trajectory optimizers [40], graph search [17], and motion primitives [13]. We use a two-layer approach that combines graph search with motion primitives.…”