The expanding space economy has created an urgent need for reliable on-orbit transportation systems to support both commercial and scientific missions. This paper explores a cooperative approach to orbital transportation, where a swarm of spacecraft agents, each securely attached to a rigid object, collaboratively transport it in orbit while adhering to directional constraints on their control inputs. A bi-level optimization framework is proposed to address this problem in a fuel-efficient and computationally feasible manner. The approach is structured with an inner problem, rapidly solved through convex optimization, and an outer problem, optimized with population-based heuristic methods—specifically, a genetic algorithm and particle swarm optimization. The proposed approach is evaluated against a state-of-the-art nonlinear solver, and demonstrates several advantages over traditional methods, including faster convergence, better scalability to large numbers of agents, lower constraint violation and reduced fuel consumption.
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