Mission Scheduling and Optimization Algortihm For Small Satellite Constellations

CubeSats, a class of small satellite, offer a unique opportunity for training, technology demonstrations, Earth observation, and other space-based research. There has been a recent increase in their design and implementation in private industry. Private industries and agencies have begun to research and implement larger amounts of small satellites working together, referred to as a constellation. CubeSat Constellation missions use multiple satellites to complete complex and challenging tasks instead of one larger satellite. One of the keys to mission success for CubeSat constellation missions is mission scheduling. When implementing a large network of satellites in constellation operation, scheduling becomes a challenge due to the large amount of conflicts that need to be resolved. Conflicts occur when more than one satellite and/or ground resource can be used to complete a task. The following thesis describes and demonstrates an advanced mission scheduling algorithm to schedule Earth observation, data transfer, or relief aid missions. Each type of mission is given a test case and results show the algorithms weighted-sum flexibility to solve multiple mission objectives. The weighted-sums optimization algorithm is used to test if the effectiveness of the chosen design variables (transfer time, age of data, spatial coverage, and temporal coverage) and to test if the blended objective functions are effective. This thesis presents the preliminary results of the mission scheduling and selection of ideal weighting of different objectives for CubeSat constellation missions.