WebAbstract: In this paper, we combine the concepts of hyper-volume, ant colony optimization and nondominated sorting to develop a novel multi-objective ant colony optimizer for global space trajectory optimization. In particular, this algorithm is first tested on three space trajectory bi-objective test problems: an Earth-Mars transfer, an Earth-Venus transfer … WebAnt Colony Optimizer for Space Trajectory Optimization 1st Giacomo Acciarini Department of Mechanical and Aerospace Engineering University of Strathclyde 75 Montrose Street, G1 1XJ Glasgow, United Kingdom [email protected] 2nd Dario Izzo Advanced Concepts Team European Space Research and Technology Center
apollo/open_space_trajectory_optimizer_en.md at master - Github
Webtrajectory design space. An outer-loop, multi-objective optimizer based on the non-dominated sorting genetic algorithm II (NSGA-II)8 drives the inner loop to identify a set of Pareto-optimal solutions. The outer loop * Senior Systems Engineer, a.i. solutions, Inc., 4500 Forbes Blvd., Suite 300, Lanham, MD, 20706, USA. Web20 de jul. de 2024 · Another main direction is to make changes into the pheromone matrix; for instance, MHACO (Acciarini et al., 2024) sorts the solutions of by hypervolume scores to solve space trajectory bi ... iowa innovation
10 - Swarming Theory Applied to Space Trajectory Optimization
Web6 de dez. de 2010 · Introduction. The determination of optimal (either minimum-time or minimum-propellant-consumption) space trajectories has been pursued for decades with … Web10 de set. de 2024 · Download PDF Abstract: We present a framework for bi-level trajectory optimization in which a system's dynamics are encoded as the solution to a constrained optimization problem and smooth gradients of this lower-level problem are passed to an upper-level trajectory optimizer. This optimization-based dynamics … Web3 de out. de 2024 · These are trained in simulation and transferred to a physical Solo 8 quadruped robot without further adaptation. In particular, we explore the space of feed-forward designs afforded by the trajectory optimizer to understand its impact on RL learning efficiency and sim-to-real transfer. iowa innovation grant