The SENSE project is articulated in three Actions. Below is the description of the work performed within the project; refer to the technical report for more details.
Action 1: Product deployment and optimization: Hardware-in-the-loop Testing
An additional high-resolution screen, a new set of collimating lenses, and an optical camera have been procured and added to the EXTREMA single-line optical bench to test the performances of the double-optic sensor. The attached figures show the model of the experimental setup devised in the proposal, as well as the final optical facility assembled in the lab. The second optical line screen displays the scenario observed by the second optic. While the proposal recommends positioning the two optics at a 90 deg angle for optimal configuration, although this setup may not always be feasible due to operational constraints. Depending on the interplanetary trajectory covered by the probe, one or both optimal planets may not be visible due to their vicinity to the Sun or their low luminosity. To explore potential improvements, we conducted an additional preliminary study, analyzing the navigation algorithm performance with variations in the configuration angle and field of view (FoV) of the two optics. A universally optimal strategy or sensor configuration cannot be reached. Yet, proper tuning of the two-optic disposition angle must be performed during the mission design phase to achieve the best navigation performance.
Moreover, the navigation filter has been updated to consider measurements coming from both the camera optics to correct the spacecraft state estimate. Preliminary software simulation showed that over a 100-day leg on an Earth-Mars transfer, the accuracy in the probe state estimation increased by almost 50%. Successively, the image processing procedure and the navigation filter have been deployed on a miniaturized processor representative of a CubeSat onboard computer.
Action 2: Validation – Autonomous Navigation Sensor Validation
The sensor validation has been performed step-by-step due to the additional complexity introduced by the presence of the hardware. As the first step, the image processing pipeline was tested by acquiring images from the optical bench and processing them on a Raspberry Pi. The test was successfully completed, validating the operability of the chosen procedure on a computationally limited computer. Subsequently, the navigation algorithm was assessed with only the optical facility in the loop. This analysis aimed to understand how the introduction of real optical errors in deep-space images would impact the performance. Finally, the navigation algorithm was deployed on a Raspberry Pi, and processor-in-the-loop simulations were performed. In this case, the images were not taken from the optical facility but were generated with a high-fidelity rendering engine. The Monte Carlo run obtained the same performance as the simulations run without the processor in the loop.
Action 3 – Exploitation and Knowledge Transfer: Market Assessment and Exploitation
This activity combined desk research aimed at outlining the knowledge landscape, the structure of the supply chain, and the market outlook of the small satellite domain and exercises/brainstorming sessions aimed at further reflecting on the value proposition of the sensor and its competitive advantages, inputs for re-examining the business proposition. For what concerns the market analysis, the outcomes were an overview of the sensor sector and market (its profile, dimension, and trend) and competitive intelligence analysis, including comparison tables to outline competitors and competitive solutions, the nanoSENSE attractiveness map, and its lean canvas.
