Title
Online adaptation of path formation in UAV search-and-identify missions
Author
van Willigen, W.H.
Schut, M.C.
Eiben, A.E.
Kester, L.J.H.M.
Publication year
2011
Abstract
In this paper, we propose a technique for optimisation and online adaptation of search paths of unmanned aerial vehicles (UAVs) in search-and-identify missions. In these missions, a UAV has the objective to search for targets and to identify those. We extend earlier work that was restricted to offline generation of search paths by enabling the UAVs to adapt the search path online (i.e., at runtime). We let the UAV start with a pre-planned search path, generated by a Particle Swarm Optimiser, and adapt it at runtime based on expected value of information that can be acquired in the remainder of the mission. We show experimental results from 3 different types of UAV agents: two benchmark agents (one without any online adaptation that we call 'naive' and one with predefined online behaviour that we call 'exhaustive') and one with adaptive online behaviour, that we call 'adaptive'. Our results show that the adaptive UAV agent outperforms both the benchmarks, in terms of jointly optimising the search and identify objectives. © 2011 Springer-Verlag.
Subject
Physics & Electronics
DSS - Distributed Sensor Systems
TS - Technical Sciences
design and engineering for self-adaptive systems
search and identify
unmanned aerial vehicles
Expected value of information
Offline
On-line adaptation
Particle swarm
Path formation
Runtimes
search and identify
Self-adaptive system
Adaptive systems
Optimization
Space shuttles
Unmanned aerial vehicles (UAV)
Adaptive algorithms
To reference this document use:
http://resolver.tudelft.nl/uuid:d2ac4697-cfd3-47b6-aab8-689a6504ab43
DOI
https://doi.org/10.1007/978-3-642-20267-4_20
TNO identifier
429746
Source
10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011, 14 - 16 April 2011, Ljubljana. (PART 2), 186-195
Series
Lecture Notes in Computer Science LNCS (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
conference paper