Experience in trajectory optimization for missions with low thrust propulsion and gravity assist maneuvers. The problem of designing an interplanetary trajectory combining low-thrust propulsion and gravity assist maneuvers has been addressed by a direct finite elements in time transcription of the resulting optimal control problem. In particular two different kind of missions were studied: a mission to the Sun and a mission to Mercury. For both the aim was to minimize the propellant consumption to reach a given target. In the first case the target was a resonant heliocentric orbit while in the second case the target was the sphere of influence of Mercury.
Trajectory optimization, through a direct method, for a mission to NEOs, using low-thrust propulsion both for deep space navigation and for Earth escape phase, departing from an Earth orbit. The aim of the work is to demonstrate the possibility to realize a low-cost mission to reach NEOs. First the escape phase has been analyzed, from perigee raising to the border of Earth sphere of influence, verifying the possibility to use the Moon for a gravity assist maneuver. Then the deep space navigation phase has been analyzed considering also more than one NEO as targets.
Optimization of low-thrust space trajectories. The problem has been formulated as the solution of an optimal control problem in which an objective function related to controls is minimized satisfying a series of constraints on the trajectory which are both differential and algebraic. The problem has been faced transcribing the differential constraints with a parallel multiple shooting transcription method into a NLP problem which has been solved with an interior point method. The method that has been developed is particularly suited for the solution of problems in which the trajectory is constrained with a great number of inequalities both on states and controls. As an example of such kind of problem the method has been applied to the design of reconfiguration maneuvers for spacecrafts flying in formation where the collision avoidance issue lead to the imposition of a large number of inequalities on states.
Analysis and design of low-energy interplanetary transfers, exploiting the invariant manifolds of the restricted three-body problem. This approach decomposes the full four-body problem describing the dynamics of an interplanetary transfer between two planets, in two three-body problems each one having the Sun and one of the planets as primaries; then the transit orbits associated to the invariant manifolds of the Lyapunov orbits are generated for each Sun-Planet system and linked by means of a Lambert's arc defined in an intermediate heliocentric two-body system. The search for optimal transit orbits is performed by means of a dynamical Poincaré section of the manifolds. A merit function, defined on the Poincaré section, is used to optimally generate a transfer trajectory given the two sections of the manifolds. Due to the high multimodality of the resulting optimization problem, an evolutionary algorithm is used to find a first guess solution which is then refined, in a further step, using a gradient method. In this way all the parameters influencing the transfer are optimized by blending together dynamical system theory and optimization techniques. The proposed patched conic-manifold method exploits the gravitational attractions of the two planets in order to change the two-body energy level of the spacecraft and to perform a ballistic capture and a ballistic repulsion. The effectiveness of this approach is demonstrated by a set of solutions found for transfers from Earth to Venus and to Mars.
Development of a mobile rover equipped with a pure optical navigation system: the navigation is guided by stereo images processing, with an algorithm based on disparity maps analysis and image segmentation. The rover is an autonomous system based on a stereo vision system for the detection of potentially dangerous obstacles. The vision system is based on two cameras forming a stereo pair which is able to extract information on the surroundings. These information are needed by the autonomous navigation algorithm which calculates a trajectory that avoids obstacles. A system based on cameras as sensors can be easily improved by an upgrade of the software used for the image analysis.
Development of the university satellite PALAMEDE: it is an educational and technological research project, aimed at involving students in the complete flow of activities typical for a space vehicle design. PALAMEDE satellite, beyond its educational purposes, is a LEO platform for Earth observation, by means of a CCD camera, and for testing attitude control innovative algorithms. The core of the team is made up of students who establish 90% of the manpower: this allows us to say that PALAMEDE is really and only a "student satellite".
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