UW Computer Science Department Strategies for Multi-Asset Surveillance Dr. William M. Spears Dimitri Zarzhitsky Suranga Hettiarachchi Wesley Kerr University.

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Presentation transcript:

UW Computer Science Department Strategies for Multi-Asset Surveillance Dr. William M. Spears Dimitri Zarzhitsky Suranga Hettiarachchi Wesley Kerr University of Wyoming

UW Computer Science Department Scenario Foliage detector Target detector Maximize the number of T targets found by α assets.

UW Computer Science Department Forest Generator L x L environment with T targets and foliage.

UW Computer Science Department Asset Control Behavior-based asset controllers. –Straight Line (SL) Assets “bounce” off boundary walls. Ignores foliage. –Straight Line Avoid Forest (SLAF) Like SL but also reverse course if encounter foliage. –Super Straight Line Avoid Forest (SSLAF) Like SLAF but move opposite to center of mass of foliage (a more sophisticated foliage sensor).

UW Computer Science Department Target Control Stationary targets for baseline study. “Hiding Gollum” target controller: –Targets try to cross from left to right through environment while hiding in foliage.

UW Computer Science Department Stationary Targets Why is SLAF so poor and SSLAF so good?

UW Computer Science Department Asset Coverage Maps SLSLAFSSLAF SL provides uniform coverage of the space. SSLAF provides increased uniform coverage of the non-foliage space. But SLAF misses entire regions.

UW Computer Science Department Gedanken Experiment What if the targets move slowly from left to right? Will the prior results change?

UW Computer Science Department Gollum Targets Why is SLAF so good?

UW Computer Science Department Probabilistic Analysis Controller 1: Uniformly cover whole area (like SL). Controller 4: Uniformly cover one row (worst case SLAF). Controller 2: Uniformly cover one column (best case SLAF). Controller 3: Uniformly cover one diagonal (average case SLAF).

UW Computer Science Department Area Controller Expected number of time steps for asset to cover area. Visibility time of target.

UW Computer Science Department Column Controller

UW Computer Science Department Diagonal Controller

UW Computer Science Department Row Controller

UW Computer Science Department Comparison of Controllers SLAF works well on moving targets because diagonal controller performance is like column controller performance.

UW Computer Science Department Summary Developing predictive mathematical theory for multiple assets performing surveillance. –Currently includes number of assets, their speed, target speed, and environment size. –Working on including probability of detection (a noisy sensor), percentage of foliage, and time limits on mission length. Goal is to provide mathematical tools to yield an optimal strategy for a surveillance mission.