Minimization of Fuel Consumption of A Swarm Of Spacecraft Through A Genetic Algorithm Approach


As humanity moves closer to forming realis-tic paths toward space exploration beyond that what we have already accomplished, multiple new chal-lenges have presented themselves. Traditional large spacecraft prove to be unfeasible both logistically and economically for missions where a single prob-lem can completely halt operations, especially given that higher reward missions are also of higher risk. A possible alternative to large craft is using a swarm of smaller craft made to accomplish the same goals while mitigating some of the drawbacks large craft face. Rockets, space shuttles, and satellites all prove to be too large to navigate areas of space dense with obstacles. Smaller craft on the scale of one meter in a large swarm would navigate these regions. Due to the decentralized nature of a swarm, any problems faced by one craft do not necessarily affect the oth-ers, allowing the swarm to stay operational despite some crafts becoming compromised. This feature means that a problem or miscalculation that could completely derail an entire mission in the context of a large spacecraft would not do the same to a swarm. In the context of exploring dense and/or extreme en-vironments in space, many logistic and economic problems faced by large craft due to their size and centralized nature will not affect a swarm. With an ac-curate mathematical model of the swarm dynamics from Benet et al.[1], a genetic algorithm’s metaheuristic method is utilized[2] to find optimal pa-rameters that yield a minimal fuel consumption value for a given trajectory/mission objective. From this approach, the total fuel consumption was cut in half while retaining desirable characteristics of the trajec-tory such as collision avoidance and final formation constraints, giving us a similar course that accom-plishes the same goal of transporting craft around objects and disturbances while also minimizing eco-nomic losses.
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