Recent breakthroughs in technology have led to a thriving "new space" culture in low-Earth orbit (LEO) in which performance and cost considerations dominate over resilience and reliability as mission goals. These advances create a manifold of opportunities for new research and business models but come with a number of striking new challenges. In particular, the size and weight limitations of low-Earth orbit small satellites make their successful operation rest on a fine balance between solar power infeed and the power demands of the mission payload and supporting platform technologies, buffered by on-board battery storage. At the same time, these satellites are being rolled out as part of ever-larger constellations and mega-constellations. Altogether, this induces a number of challenging computational problems related to the recurring need to make decisions about which task each satellite is to effectuate next. Against this background, GomSpace and Saarland University have joined forces to develop highly sophisticated software-based automated solutions rooted in optimal algorithmic and self-improving learning techniques, all this validated in modern nanosatellite networked missions operating in orbit.
翻译:最近技术方面的突破导致低地轨道上“新空间”文化蓬勃发展,其中性能和成本方面的考虑主导了作为飞行任务目标的复原力和可靠性,这些进步为新的研究和商业模式创造了许多机会,但带来了一些惊人的新挑战,特别是低地轨道小型卫星的大小和重量限制,使得其成功运行取决于太阳能被淹没与飞行任务有效载荷和辅助平台技术的动力需求之间的细微平衡,由机载电池储存缓冲。与此同时,这些卫星正在作为日益扩大的星座和超大型星座的一部分被推出。总的来说,这引起了一些具有挑战性的计算问题,因为经常需要就每颗卫星的下一个任务作出决定。在这种背景下,Gomspace和Saarland大学联手开发了基于最佳算法和自我改进学习技术的高度精密的软件自动解决方案,所有这些都在轨道运行的现代超小型卫星联网飞行任务中得到验证。