LiU Electronic Press
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Author:
Kolling, Andreas (Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems) (Linköping University, The Institute of Technology) (Collaborative Robotics)
Kleiner, Alexander (Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems) (Linköping University, The Institute of Technology) (Collaborative Robotics)
Title:
Multi-UAV Trajectory Planning for Guaranteed Search
Department:
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems
Linköping University, The Institute of Technology
Publication type:
Conference paper (Refereed)
Language:
English
In:
Proc. of the 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2013)
Conference:
12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)
Pages:
79-86
Year of publ.:
2013
URI:
urn:nbn:se:liu:diva-87289
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-87289
ISBN:
978-1-4503-1993-5
Subject category:
Computer Science
Computer Vision and Robotics (Autonomous Systems)
Project:
Artificial Intelligence & Integrated Computer Systems
Abstract(en) :

We consider the problem of detecting all moving and evading targets in 2.5D environments with teams of UAVs. Targets are assumed to be fast and omniscient while UAVs are only equipped with limited range detection sensors and have no prior knowledge about the location of targets. We present an algorithm that, given an elevation map of the environment, computes synchronized trajectories for the UAVs to guarantee the detection of all targets. The approach is based on coordinating the motion of multiple UAVs on sweep lines to clear the environment from contamination, which represents the possibility of an undetected target being located in an area. The goal is to compute trajectories that minimize the number of UAVs needed to execute the guaranteed search. This is achieved by converting 2D strategies, computed for a polygonal representation of the environment, to 2.5D strategies. We present methods for this conversion and consider cost of motion and visibility constraints. Experimental results demonstrate feasibility and scalability of the approach. Experiments are carried out on real and artificial elevation maps and provide the basis for future deployments of large teams of real UAVs for guaranteed search.

Research funder:
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, 1025
Available from:
2013-01-16
Created:
2013-01-15
Last updated:
2013-08-08
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141 hits
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