LiU Electronic Press
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Author:
Wzorek, Mariusz (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Conte, Gianpaolo (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Rudol, Piotr (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Merz, Torsten (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group )
Duranti, Simone (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group )
Doherty, Patrick (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab)
Title:
From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle
Department:
Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proceedings of the 21st Bristol UAV Systems Conference (UAVS)
Year of publ.:
2006
URI:
urn:nbn:se:liu:diva-36792
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-36792
Local ID:
32592
Subject category:
Computer Science
SVEP category:
Computer science
Abstract(en) :

The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. While the application domains in which they are currently used are still predominantly military in nature, in the future we can expect wide spread usage in thecivil and commercial sectors. In order to insert such vehicles into commercial airspace, it is inherently important that these vehicles can generate collision-free motion plans and also be able to modify such plans during theirexecution in order to deal with contingencies which arise during the course of operation. In this paper, wepresent a fully deployed autonomous unmanned aerial vehicle, based on a Yamaha RMAX helicopter, whichis capable of navigation in urban environments. We describe a motion planning framework which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Treestogether with a path following controller that is used during path execution. Integrating deliberative services, suchas planners, seamlessly with control components in autonomous architectures is currently one of the major open problems in robotics research. We show how the integration between the motion planning framework and thecontrol kernel is done in our system.

Additionally, we incorporate a dynamic path reconfigurability scheme. It offers a surprisingly efficient method for dynamic replanning of a motion plan based on unforeseen contingencies which may arise during the execution of a plan. Those contingencies can be inserted via ground operator/UAV interaction to dynamically change UAV flight paths on the fly. The system has been verified through simulation and in actual flight. We present empirical results of the performance of the framework and the path following controller.

Available from:
2009-10-10
Created:
2009-10-10
Last updated:
2012-01-19
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