Robust search for structured object placement using unmanned vehicles

Abstract

We develop a robust method for planning an undersea search by unmanned vehicles; the goal of which is to find groups of objects that may be placed randomly or arranged in predefined patterns on the ocean floor. This approach revolves around the computation of target pattern priors that describe the underlying geometrical structure of the expected group of objects. These priors are updated based on limited search observations, followed by a search performance assessment in the remaining region utilizing these observed target pattern distributions. We outline the derivation of this approach to assessing the performance of unmanned searches. The algorithms performance is tested in a simulation example to illustrate the improvement in search evaluation accuracy provided by the consideration of object structure. We conclude with a discussion of how this search planning strategy can be employed in realistic scenarios, searching for objects that are expected to appear in more general patterns.

Multi-sensor area coverage analysis for aspect-dependent targets

“…where r = range to target, and = target aspect relative to sensor and p d2 (r, ) = probability-of-detection for sensor 2 (2) For each sensor, if we consider all detection opportunities as independent Bernoulli trials [4], the probability of detecting a target at least once using the non-aggregate sensor performances as defined in Equations (1) and (2) is ( 3 ) Now define the typical aggregate sensor performances (4) and (5) where f( ) = probability density function of target aspect (6)…”

“…However, Scenario (1) detects significant p(k 1) variability as well as significantly different average p(k 1) over aspect angle. Scenario (2) shows no variability over aspect and it is fact incapable of detecting any such variability. Furthermore, Scenario (2) underestimates p(k 1)'s standard deviation by 37%.…”

“…Current approaches for mission planning and performance analysis do not accommodate aspect-dependent sensor performance [1,2,3]. Instead, sensor performance functions only represent aggregate performance over uniformly distributed target orientations.…”

“…The new age of mine countermeasures (MCM) is likely to be dominated by remotely operated or autonomous underwater vehicles (AUVs) equipped with multiple on-board sensors [2,3]. Teams of such vehicles are not constrained to simple coverage patterns with rows of parallel tracks created by using the mow-the-lawn coverage approach.…”