A canonical problem for autonomy is search and discovery. Often, searching needs to be unpredictable in order to be effective. In this paper, we investigate and compare the effectiveness of the traditional and predictable lawnmower search strategy to that of a random search. Specifically the family of searches with paths determined by heavy tailed distributions called Lévy stable searches is investigated. These searches are characterized by long flight paths, followed by a new random direction, with the flight path lengths determined by the distribution parameter α. Two basic search scenarios are considered in this study: stationary targets, and moving targets, both on planar surfaces. Monte-Carlo simulations demonstrate the advantages of Lévy over the lawnmower strategy especially for moving targets. Ultimately to corroborate the suitability of the Lévy strategy for UAVs, we implement and demonstrate the feasibility of the algorithm in the Multiple Unified Simulation Environment (MUSE), which includes vehicle's constraints and dynamics. The MUSE / Air Force Synthetic Environment for Reconnaissance and Surveillance (AFSERS) simulation system is the primary virtual ISR and UAV simulation within DOD for command and staff level training for the Joint Services.
[1]
Andrea L. Bertozzi,et al.
Multi-scale Collaborative Searching through Swarming
,
2010,
ICINCO.
[2]
Andrea L. Bertozzi,et al.
Multi-Vehicle Flocking: Scalability of Cooperative Control Algorithms using Pairwise Potentials
,
2007,
Proceedings 2007 IEEE International Conference on Robotics and Automation.
[3]
A. Bertozzi,et al.
Pattern Formation Stability and Collapse in 2D Driven Particle Systems
,
2006
.
[4]
H. Stanley,et al.
Lévy flights in random searches
,
2000
.
[5]
L. Stone.
Theory of Optimal Search
,
1975
.
[6]
I. Mezić,et al.
Metrics for ergodicity and design of ergodic dynamics for multi-agent systems
,
2011
.
[7]
B. O. Koopman,et al.
Search and its Optimization
,
1979
.