Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-24T07:56:18.804Z Has data issue: false hasContentIssue false

Exploring the Performance of Natural Search Strategies for the Control of Unmanned Autonomous Vehicles

Published online by Cambridge University Press:  12 March 2009

Alec Banks*
Affiliation:
(Software Systems Research Centre, Bournemouth University, UK)
Jonathan Vincent
Affiliation:
(Software Systems Research Centre, Bournemouth University, UK)

Abstract

This paper builds on prior research into the application of particle swarm optimisation to autonomous vehicle control in search roles. It examines the use of naturally inspired search strategies to enhance the performance of groups of sensor-based vehicles in applications where there is no knowledge a priori regarding target presence, location, distribution or behaviour (movement). This paper first briefly reviews existing ethological research into search strategies in the natural world, identifying three types of random walk, two multi-phase strategies and two species-specific strategies for further investigation. Experiments are then performed within a simulation environment to compare the performance of naturally inspired strategies with deterministic patterns and random movement, when searching for both static and dynamic targets. Results indicate that performance improvements can be realised, provided that critical relationships within the application domain broadly match those existing in the underlying natural metaphor.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

AdaCore, (2005). Ada Academic Initiative. Available from: http://www.adacore.com/academic_overview.php [accessed 22 June 2007].Google Scholar
Banks, A, Vincent, J and Phalp, K (2008a). Particle Swarm Guidance System for Autonomous, Unmanned Aerial Vehicles in an Air Defence Role. The Journal of Navigation, 61, 929.CrossRefGoogle Scholar
Banks, A, Vincent, J and Phalp, J (2008b). Natural strategies for search. To appear, Natural Computing, Springer.CrossRefGoogle Scholar
Baum, KA and Grant, WE (2001). Hummingbird foraging behavior in different patch types: simulation of alternative strategies. Ecological Modelling, 137, 201209.CrossRefGoogle Scholar
Bell, WJ (1991) Searching behavior: The behavioral ecology of finding resources. Chapman and Hall, New York, 1991.Google Scholar
Benichou, O, Coppey, M, Moreau, M, Suet, P-H and Voituriez, R (2005). Optimal search strategies for hidden targets. Phys. Rev. Lett. 94, 198101.CrossRefGoogle ScholarPubMed
Budick, SA and Dickinson, MH (2006). Free-flight Responses of Drosophila melanogaster to Attractive Odors. The Journal of Experimental Biology, 209, 30013017.CrossRefGoogle ScholarPubMed
Charnov, EL (1976). Optimal Foraging, the Marginal Value Theorem. Theoretical Population Biology, 9, April 1976.CrossRefGoogle ScholarPubMed
English, J (2000). About JEWL. Available from: http://www.it.bton.ac.uk/staff/je/jewl/ [Accessed 22 June 2007].Google Scholar
Hill, S, Burrows, MT and Hughes, RN (2000). Increased turning per unit distance as an area-restricted search mechanism in a pause-travel predator, juvenile plaice, foraging for buried bivalves. Journal of Fish Biology, 56, 14971508.Google Scholar
Hinde, RA (1956). The biological significance of the territories of birds. Journal of Fish Biology, 98, 340369.Google Scholar
Krakauer, DC and Rodríguez-Gironés, MA (1995). Searching and learning in a random environment. J. Theor. Biol., 177, 417419.CrossRefGoogle Scholar
Lode, T (2000). Functional response and area-restricted search in a predator: seasonal exploitation of anurans by the European polecat, Mustela putorius. Austral Ecology 25, 223231.CrossRefGoogle Scholar
O'Brien, JW, Browman, HI, Evans, BI (1990). Search Strategies of Foraging Animals. American Scientist, 78, 152160.Google Scholar
Passino, KM (2002). Biomimicry of bacterial foraging for distributed optimisation and control. IEEE Control Systems Magazine, June, 52–67.Google Scholar
Reynolds, AM (2005). Scale-free movement patterns arising from olfactory-driven foraging. Physical Review E 72, 041928.CrossRefGoogle ScholarPubMed
Secor, SM (1994). Ecological Significance of Movements and Activity Range for the Sidewinder, Crotalus cerastes. Copeia, Vol. 1994, No. 3. (Aug. 17, 1994), 631645.CrossRefGoogle Scholar
Stahl, JC and Sagar, PM (2000). Foraging strategies if southern Buller's albatrosses Diomedea b. bulleri breeding on The Snares, New Zealand. Journal of The Royal Society of New Zealand, 30, 299318.CrossRefGoogle Scholar
Tammero, LF and Dickinson, MH (2002). The influence of visual landscape on the visual behavior of the fruit fly Drosophila melanogaster. The Journal of Experimental Biology 205, 327343.CrossRefGoogle ScholarPubMed
Viswanathan, GM, Buldyrev, SV, Havlin, S, Da Luz, MGE, Raposo, EP, Stanley, HE (1999). Optimizing the success of random searches. Nature 401, 911914.CrossRefGoogle ScholarPubMed
Zamon, JE (2001). Seal predation on salmon and forage fish schools as a function of tidal currents in the San Juan Islands, Washington, USA. Fisheries Oceanography, 10:4, 353366.CrossRefGoogle Scholar