Mission scenarios beyond line of sight or with limited ground control station access require capabilities for autonomous safe navigation and necessitate a continuous extension of existing and potentially outdated information about obstacles. The presented approach is a novel synthesis of techniques for 3D environment perception and global path planning. A locally bounded sensor fusion approach is used to extract sparse obstacles for global incremental path planning in an anytime fashion. During the flight, a stereo camera checks the field of view along the flight path ahead by analyzing depth images. A 3D occupancy grid is built incrementally. To reduce the high data rate and storage demands of grid-type maps, an approximated polygonal world model is created. For a compacted representation, it uses prisms and ground planes. This enables the system to constantly renew and update its knowledge about obstacles. An incremental heuristic path planner uses both a-priori information as well as incremental obstacle updates to assure a collision-free path at any time. Mapping results from flight tests show the functionality of onboard world modeling from real sensor data. Path planning feasibility is demonstrated within a simulation environment considering world model changes inside the vehicle's field of view.
Online world modeling and path planning for an unmanned helicopter
Online-Weltmodellierung und Wegplanung für einen unbemannten Hubschrauber
Autonomous Robots ; 27 , 3 ; 147-164
2009
18 Seiten, 41 Quellen
Aufsatz (Konferenz)
Englisch
Online world modeling and path planning for an unmanned helicopter
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