Abstract
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.
Abstract (translated)
对航天应用有效利用计算机视觉和机器学习的限制是由计算能力有限所制成的,因此性能也受到限制。虽然使用ARM处理器的嵌入式系统已经表现出能够符合但性能标准不高的情况,但最近可用的更大的空间等级可编程门阵列(FPGA)显示了超过微型计算机系统性能的潜力。这项工作提出了使用基于神经网络的对象检测算法,该算法可以在同样资源受限的FPGA上部署,以自动检测在轨道上的不合作卫星的组件。在佛罗里达州技术大学进行的硬件循环实验在ORION运动控制模拟器上进行了测试,以比较在新部署的FPGA上的新模型在微型计算机系统上的等价算法的性能。结果表明,FPGA实现增加了吞吐量并减少了延迟,同时保持了同等精度。这些发现建议未来任务应考虑在空间等级FPGA上部署计算机视觉算法。
URL
https://arxiv.org/abs/2301.09055