Earth science benefits tremendously from spaceborne synthetic aperture radar. By combining multiple images taken from different angles, we can create accurate digital elevation models and high-resolution tomograms that unveil the three-dimensional structure of vegetation, ice, and dry soil. Whereas today such images are acquired sequentially with conventional satellites, compromising product quality and hindering the monitoring of fast dynamics, DRITUCS envisions distributed sensor concepts to acquire all data in a single pass, paving the way for effective and powerful monitoring of our planet. We exploit clusters of smallsats and build high-quality products from noisy and undersampled data. This makes a key contribution to multi-dimensional imaging theory and represents a paradigm shift from state-of-the-art techniques that demand expensive, high-quality imagery to create digital elevation models and tomograms. Smallsats can be mass-manufactured and lead to low-cost solutions. They are a disruptive NewSpace technology that needs to be complemented by novel distributed approaches to replace and enhance large aperture, high power radar systems. We are pursuing three scientific paths to lay the foundations of a) distributed multi-baseline interferometry, b) distributed tomography, and c) multiple-input multiple-output tomography that takes advantage of waveform diversity to infer unique information about different scattering mechanisms in natural and man-made environments. The elaboration of theoretical models and the development of signal processing algorithms will be complemented by experimental demonstrations with drones. DRITUCS is a giant leap for radar remote sensing with a significant impact on numerous applications. It will pose the basis for future advanced Earth observation missions that will offer remarkable societal benefits and boost European capabilities in the emerging NewSpace sector.

The aim of the RECOVER.ME project is to achieve human ingenuity in dealing with hardware faults in robotic space exploration. The hypothesis of the project is, that as robots acquire human-like metacognitive awareness and metacognitive regulatory abilities, they will be enabled to recover from severe but rectifiable hardware malfunction all by themselves. This is of particular importance to planetary exploration, as a hardware fault need not be the end of a mission. However, as of today, once a hardware malfunction occurs, the remote robot is typically taken out of operation and troubleshooting is done manually. In the future, especially, when more complex robots are deployed to construct planetary infrastructure for crewed exploration, this can no longer be tolerated. Considering that a hardware fault may occur at any time, such a situation can become safety-critical for the robot, the established infrastructure, and for astronauts in the vicinity of the robot. To overcome this issue, RECOVER.ME proposes a novel approach for metacognition-enabled failure handling. Instead of relying on hard-coded recovery strategies by specifying how a robot has to react to a certain sub-system fault, the project aims to bootstrap failure handling as a property of the cognitive architecture of the robot itself. Metacognitive awareness is created through a novel knowledge representation that describes how hardware faults may impact robot capabilities. Metacognitive planning will yield contingency configurations employing abstract, affordance-based first order-logic planning for self-programming. To empower robots to monitor their own programming and evaluate the best strategy to react to arbitrary failure cases, generic limitation models will translate sub-symbolic fault information into semantically interpretable knowledge for metacognitive monitoring and metacognitive evaluation. This will provide robots with competent strategies to deal with faults in a similar way to humans.

This research aims to develop a unified framework to compute in real-time optimal guidance solutions by merging two of the most promising technologies arisen over the last years, that is, pseudospectral optimal control, and convex optimization. The rationale for this choice can be found in the following motivations: 1. The former theory has very interesting properties, such as the quasi-exponential convergence to the true optimal solutions, and was already used to re-orient the International Space Station in 2006, leading to a save of about 1,000,000$ in terms of required propellant with respect to the previous methods. 2. Convex-optimization provides the technology to solve optimal control problems in real-time, a key feature for the future space systems, and computes the global optimum. 3. The two technologies are complementary as each method’s drawbacks are counterbalanced by the other method’s strengths, and their unification will yield an improvement of performance since the solutions will be optimal, in the sense of maximizing or minimizing a given criterion, while nowadays only sub-optimal schemes are available. 4. The research outcome will find applications in several industrial fields, leading to beneficial effects outside the space engineering field as well. The hybrid approach will consist in transcribing the original optimal control problem by using pseoudospectral transcription, that is, by adopting differential, integral, and discretization operators coming from pseudospectral methods. The resulting discrete problem will be then posed in convex form, suitable for real-time applications. The research will focus on the theoretical and algorithmic part, to be developed at the San Diego State University with Prof. Ping Lu during the first two years of program, while the third year will be spent at the German Aerospace Center, where the results will be implemented on a real-time architecture to show the maturity achieved by the proposed method.