Spacecraft Trajectory Optimization
A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".
Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 506
Special Issue Editor
Special Issue Information
Dear Colleagues,
Trajectory optimization is a fundamental step in the definition of a space mission. In the recent past, we have seen the introduction and diffusion of new technologies, such as electric propulsion and micro-satellites/CubeSats, and missions to many different targets, from Mars and giant planets and their moons, to near-Earth asteroids, to Lagrangian points. The next steps for the colonization of space consider a space/lunar gateway that must be connected with LEO and the Moon surface and could see the departure of interplanetary missions. This continuous evolution creates new challenges that need to be faced by the space trajectory optimization community. Traditional direct and indirect optimization methods have seen an increase in capabilities and have broadened their fields of application. Heuristic/evolutionary algorithms have also become useful optimization tools. New mission concepts, such as satellite servicing/refurbishing and removal of orbital debris, could be implemented in the near future and may also require the solution of combinatorial problems.
This Special Issue aims to provide an overview of recent advances in space trajectory optimization methods and/or their application to novel mission concepts. Authors are invited to submit full research articles and review manuscripts addressing (but not limited to) the following topics:
Trajectories in proximity of the Earth (LEO, MEO);
Trajectories to/in GEO;
Missions to/from Lagrangian points;
Interplanetary missions;
Asteroid missions;
Debris removal;
Satellite servicing;
Formation flying.
Prof. Dr. Lorenzo Casalino
Guest Editor
Manuscript Submission Information
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Keywords
- Space trajectory optimization
- Direct optimization methods
- Indirect optimization methods
- Evolutionary algorithms
