Proceedings of SPIE Defense & Security, Orlando, FL (April 2011)
Unmanned aerial systems (UAS) have proven themselves to be indispensable in providing intelligence, surveillance, and reconnaissance (ISR) over the battlefield. Constellations of heterogeneous, multi-purpose UAS are being tasked to provide ISR in an unpredictable environment. This necessitates the dynamic replanning of critical missions as weather conditions change, new observation targets are identified, aircraft are lost or equipment malfunctions, and new airspace restrictions are introduced. We present a method to generate coordinated mission plans for constellations of UAS with multiple flight goals and potentially competing objectives, and update them on demand as the operational situation changes. We use a fast evolutionary algorithm-based, multi-objective optimization technique. The updated flight routes maintain continuity by considering where the ISR assets have already flown and where they still need to go. Both the initial planning and replanning take into account factors such as area of analysis coverage, restricted operating zones, maximum control station range, adverse weather effects, military terrain value, and sensor performance. Our results demonstrate that by constraining the space of potential solutions using an intelligently-formed air maneuver network with a subset of potential airspace corridors and navigational waypoints, we can ensure global optimization for multiple objectives considering the situation both before and after the replanning is initiated. We employ sophisticated visualization techniques using a geographic information system to help the user ‘look under the hood” of the algorithms to understand the effectiveness and viability of the generated ISR mission plans and identify potential gaps in coverage.
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