Remote sample recovery is a rapidly evolving application of Small Unmanned Aircraft Systems (sUAS) for planetary sciences and space exploration. Development of cyber-physical systems (CPS) for autonomous deployment and recovery of sensor probes for sample caching is already in progress with NASA's MARS 2020 mission. To challenge student teams to develop autonomy for sample recovery settings, the 2020 NSF CPS Challenge was positioned around the launch of the MARS 2020 rover and sUAS duo. This paper discusses perception and trajectory planning for sample recovery by sUAS in a simulation environment. Out of a total of five teams that participated, the results of the top two teams have been discussed. The OpenUAV cloud simulation framework deployed on the Cyber-Physical Systems Virtual Organization (CPS-VO) allowed the teams to work remotely over a month during the COVID-19 pandemic to develop and simulate autonomous exploration algorithms. Remote simulation enabled teams across the globe to collaborate in experiments. The two teams approached the task of probe search, probe recovery, and landing on a moving target differently. This paper is a summary of teams' insights and lessons learned, as they chose from a wide range of perception sensors and algorithms.
翻译:远程取样回收是小型无人航空器系统在行星科学和空间探索方面的一种迅速演进的应用; 美国航天局2020年MARS飞行任务已经在开发自动部署和恢复传感器探测器以进行样本采集的网络物理系统(CPS),美国航天局2020年MARS飞行任务中,为了挑战学生团队发展样本采集设置的自主性,2020年SSF CPS挑战围绕MARS 2020 rover和 SUAS duo的启动进行,本文讨论了SUAS在模拟环境中进行样本采集的认知和轨迹规划。在总共5个参与的小组中,讨论了前两个小组的结果。OpenUAVA云模拟框架使这些小组得以在COVID-19大流行期间一个月的远程工作,以开发和模拟自主探索算法。远程模拟使全球各地的小组得以在实验中合作。两个小组在研究搜索、探测恢复和着陆在移动目标上的任务各不相同。本文总结了小组的洞见和经验教训,因为它们选择从广泛的传感器和算法中挑选。