Scheduling Earth Observing Satellites with Evolutionary Algorithms

A growing fleet of NASA, commercial, and foreign Earth observing satellites (EOS) uses a variety of sensing technologies for scientific, mapping, defense and commercial activities. Image collection for these satellites is planned and scheduled by a variety of software systems using many techniques. Scheduling EOS is complicated by a number of important constraints, including: power and thermal availability, limited imaging segments per orbit, time required to take each image, limited on-board data storage, transition time between look angles (slewing), revisit limitations, cloud cover, stereo pair acquisition, ground station availability and coordination of multiple satellites. We hypothesize that evolutionary algorithms can effectively schedule Earth imaging satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems and evolutionary software to solve them [Globus et al. 2001]. The model problems are: