ESA GNC Conference Papers Repository

New generations of spacecrafts are required to perform faster onboard processing. Space exploration, rendezvous services, space robotics, etc. are all growing fields in Space that require more sensors and more computational power to perform these missions. Furthermore, new sensors in the market produce better quality data at higher rates while new processors can increase substantially the computational power. Therefore, near-future spacecrafts will be equipped with large number of sensors that will produce data at rates that has not been seen before in space, while at the same time, data processing power will be significantly increased. In regards to guidance navigation and control applications, vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions such as Active Debris Removal will rely on novel high-performance avionics to support advanced image processing algorithms with large workloads. Even more complex is the case of vision-based precision landing, where there needs to high rate processing can be the tipping point of a successful mission. This new scenario of advanced Space applications and increase in data amount and processing power, has brought new challenges with it: low determinism, parallel data processing, cumbersome software development, etc. In this article, a novel approach to parallel data processing is presented that is based on state-of-the-art algorithmic trading software techniques, which is a field that underwent a similar challenge, although is a different scale, in the early 2000. The approach presented here optimizes processing resources, simplifies development and makes applications much more reliable, therefore somewhat reshaping the paradigm of embedded software engineering. Benchmarks are presented for two rather different embedded application scenarios. One is for space on-board flight software in a limited computing capabilities and the other one is for AI applications on-board. In this paper, we show that the performance and determinism of the applications increase substantially with respect to traditional multi-thread approaches in both scenarios.