We summarise the scientific and technological aspects of the Search for Anomalous Gravitation using Atomic Sensors (SAGAS) project, submitted to ESA in June 2007 in response to the Cosmic Vision 2015-2025 call for proposals. The proposed mission aims at flying highly sensitive atomic sensors (optical clock, cold atom accelerometer, optical link) on a Solar System escape trajectory in the 2020 to 2030 time-frame. SAGAS has numerous science objectives in fundamental physics and Solar System science, for example numerous tests of general relativity and the exploration of the Kuiper belt. The combination of highly sensitive atomic sensors and of the laser link well adapted for large distances will allow measurements with unprecedented accuracy and on scales never reached before. We present the proposed mission in some detail, with particular emphasis on the science goals and associated measurements and technologies.
Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the onboard decision making system. In this paper this claim is investigated in more detail training deep artificial neural networks to represent the optimal control action during a pinpoint landing, assuming perfect state information. It is found to be possible to train deep networks for this purpose and that the resulting landings, driven by the trained networks, are close to simulated optimal ones. These results allow for the design of an on-board real time optimal control system able to cope with large sets of possible initial states while still producing an optimal response.
Mathematical optimization is pervasive in all quantitative sciences. The ability to find good parameters values in a generic numerical experiment while meeting complex constraints is of great importance and, as such, has always been an active research topic of mathematics, numerics and, more recently, artificial intelligence.
We present a satellite path-planning technique able to make a set of identical spacecraft acquire a given configuration. The technique exploits a behavior-based approach to achieve an autonomous and distributed control over the relative geometry, making use of limited sensorial information. A desired velocity is defined for each satellite as a sum of different contributions coming from generic high-level behaviors. The behaviors are further defined by an inverse-dynamic calculation dubbed equilibrium shaping. We show that by considering only three different kinds of behavior it is possible to acquire a number of interesting formations, and we describe the theoretical framework needed to find the entire set. We find that by allowing a limited amount of communication the technique may be used also to form complex lattice structures. Several control feedbacks able to track the desired velocities are introduced and discussed. Our results suggest that sliding-mode control is particularly appropriate in connection with the developed technique.
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