Adler, Benjamin
[Verfasser:in]
;
Zhang, Jianwei
[Mitwirkende:r]
System Design and Real-Time Guidance of an Unmanned Aerial Vehicle for Autonomous Exploration of Outdoor Environments ; Systementwurf und Echtzeitsteuerung eines unbemannten Luftfahrzeugs zur autonomen Exploration von Außenumgebungen
Titel:
System Design and Real-Time Guidance of an Unmanned Aerial Vehicle for Autonomous Exploration of Outdoor Environments ; Systementwurf und Echtzeitsteuerung eines unbemannten Luftfahrzeugs zur autonomen Exploration von Außenumgebungen
Beteiligte:
Adler, Benjamin
[Verfasser:in]
Erschienen:
Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2014-01-01
Anmerkungen:
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Beschreibung:
The central endeavor of this thesis is the successful development and evaluation of an aerial mobile robotic system that produces precise, georeferenced, three-dimensional maps of outdoor environments by means of autonomous exploration. The system consists of a ground station and a custom-built UAV with six degrees of freedom, featuring an on-board computer, an inertial navigation system, and two 2D laser range finders. In addition to a description of the hardware architecture and individual components being used, this dissertation presents challenges and problems that arose during the construction of its hard- and software as well as optimizations applied in the course of its development. A fundamental part of this work is the distributed software architecture for in-flight sensor fusion and data analysis, with a focus on a novel, truly three-dimensional algorithm generating multiple next-best-views (NBVs). Designed for application on airborne platforms in outdoor environments, the approach works directly on raw, unstructured point clouds and can be used either indoors or outdoors with any sensor generating spatial occupancy information. Based on the generated sensor-poses and the incrementally growing point cloud, trajectories are computed for the UAV to autonomously map its environment. To ensure safe operation, collision avoidance constantly monitors the planned path and updates it whenever obstacles are detected. In order to satisfy real-time constraints, all algorithms are implemented on a highly parallel SIMD architecture found in modern GPUs, allowing for extremely fast motion planning and responsive visualization. As the underlying hardware imposes limitations with regards to memory access and concurrency, necessary data structures and further performance considerations are explained in detail. Data has been captured during real, autonomous flights and is used to analyze the performance of all major components (flight controller, next-best-view generation, dynamic path planning and collision avoidance) ...