Daniel W. Stouch
Ernest W. Zeidman
ABSTRACT
Our SPARTEN (Spatially Produced Airspace Routes from Tactical Evolved Networks) tool generates coordinated mission plans for constellations of unmanned aerial vehicles by allowing the mission planner to specify the importance of each objective for each mission. Using an evolutionary algorithm-based, multi-objective optimization technique, we consider factors such as area of analysis coverage, restricted operating zones, maximum ground control station range, adverse weather effects, military terrain value, airspace collision avoidance, path linearity, named area of analysis emphasis, and sensor performance. By employing novel visualizations using geographic information systems to represent their effectiveness, we help the user "look under the hood" of the algorithms and understand the viability and effectiveness of the mission plans to identify coverage gaps and other inefficiencies. In this paper, we apply multi-objective evolutionary algorithms to the air mission planning domain, with a focus on the visualization components.
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Index Terms
- Coevolving collection plans for UAS constellations
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