An evolutionary approach for the target search problem in uncertain environment

M. Barkaoui (), J. Berger and A. Boukhtouta
Additional contact information
M. Barkaoui: Université Laval
J. Berger: Defence Research Development Canada - Valcartier
A. Boukhtouta: Defence Research Development, Canada - CORA

Journal of Combinatorial Optimization, 2019, vol. 38, issue 3, No 10, 808-835

Abstract: Abstract Search path planning is critical to achieve efficient information-gathering tasks in dynamic uncertain environments. Given task complexity, most proposed approaches rely on various strategies to reduce computational complexity, from deliberate simplifications or ad hoc constraint relaxation to fast approximate global search methods utilization often focusing on a single objective. However, problem-solving search techniques designed to compute near-optimal solutions largely remain computationally prohibitive and are not scalable. In this paper, a new information-theoretic evolutionary anytime path planning algorithm is proposed to solve a dynamic search path planning problem in which a fleet of homogeneous unmanned aerial vehicles explores a search area to hierarchically minimize target zone occupancy uncertainty, lateness, and travel/discovery time respectively. Conditioned by new observation outcomes and request events, the evolutionary algorithm episodically solves an augmented static open-loop search path planning model over a receding time horizon incorporating any anticipated information feedback. The proposed approach has shown to outperform alternate myopic and greedy heuristics, significantly improving relative information gain at the expense of modest additional travel cost.

Keywords: Evolutionary algorithms; Target search; Unmanned aerial vehicle; Dynamic path planning (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10878-019-00413-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:38:y:2019:i:3:d:10.1007_s10878-019-00413-1

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-019-00413-1

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().