The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to meet acceptable but low performance standards, the recent availability of larger space-grade field programmable gate arrays (FPGAs) show potential to exceed the performance of microcomputer systems. This work proposes use of neural network-based object detection algorithm that can be deployed on a comparably resource-constrained FPGA to automatically detect components of non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare the performance of the new model deployed on a small, resource-constrained FPGA to an equivalent algorithm on a microcomputer system. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy. These findings suggest future missions should consider deploying computer vision algorithms on space-grade FPGAs.
翻译:利用ARM处理器的嵌入系统已证明符合可接受但低性能标准,但最近提供的大型空间级外地可编程门阵列显示,其潜力超过微型计算机系统的性能。这项工作提议使用神经网络的物体探测算法,这种算法可部署在一个资源限制的可比较的FPGA上,以自动探测轨道上不合作卫星的部件。在佛罗里达技术局的ORION Maneuver Kinematics模拟器上进行了硬件即时实验,将部署在小型、资源限制的FPGA上的新模型的性能与微型计算机系统的等同算法作比较。结果显示,FPGA的采用可提高吞吐量和降低惯性,同时保持类似的准确性。这些结论表明,今后的任务应考虑在空间级FPGA中部署计算机视算法。