Albee Keenan, Oestreich Charles, Specht Caroline, Terán Espinoza Antonio, Todd Jessica, Hokaj Ian, Lampariello Roberto, Linares Richard
Space Systems Laboratory (SSL) and Astrodynamics, Space Robotics and Controls Lab (ARCLab), Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, United States.
Guidance & Control Group, The Charles Stark Draper Laboratory, Inc., Cambridge, MA, United States.
Front Robot AI. 2021 Sep 17;8:641338. doi: 10.3389/frobt.2021.641338. eCollection 2021.
Accumulating space debris edges the space domain ever closer to cascading Kessler syndrome, a chain reaction of debris generation that could dramatically inhibit the practical use of space. Meanwhile, a growing number of retired satellites, particularly in higher orbits like geostationary orbit, remain nearly functional except for minor but critical malfunctions or fuel depletion. Servicing these ailing satellites and cleaning up "high-value" space debris remains a formidable challenge, but active interception of these targets with autonomous repair and deorbit spacecraft is inching closer toward reality as shown through a variety of rendezvous demonstration missions. However, some practical challenges are still unsolved and undemonstrated. Devoid of station-keeping ability, space debris and fuel-depleted satellites often enter uncontrolled tumbles on-orbit. In order to perform on-orbit servicing or active debris removal, docking spacecraft (the "Chaser") must account for the tumbling motion of these targets (the "Target"), which is oftentimes not known . Accounting for the tumbling dynamics of the Target, the Chaser spacecraft must have an algorithmic approach to identifying the state of the Target's tumble, then use this information to produce useful motion planning and control. Furthermore, careful consideration of the inherent uncertainty of any maneuvers must be accounted for in order to provide guarantees on system performance. This study proposes the complete pipeline of rendezvous with such a Target, starting from a standoff estimation point to a mating point fixed in the rotating Target's body frame. A novel visual estimation algorithm is applied using a 3D time-of-flight camera to perform remote standoff estimation of the Target's rotational state and its principal axes of rotation. A novel motion planning algorithm is employed, making use of offline simulation of potential Target tumble types to produce a look-up table that is parsed on-orbit using the estimation data. This nonlinear programming-based algorithm accounts for known Target geometry and important practical constraints such as field of view requirements, producing a motion plan in the Target's rotating body frame. Meanwhile, an uncertainty characterization method is demonstrated which propagates uncertainty in the Target's tumble uncertainty to provide disturbance bounds on the motion plan's reference trajectory in the inertial frame. Finally, this uncertainty bound is provided to a robust tube model predictive controller, which provides tube-based robustness guarantees on the system's ability to follow the reference trajectory translationally. The combination and interfaces of these methods are shown, and some of the practical implications of their use on a planned demonstration on NASA's Astrobee free-flyer are additionally discussed. Simulation results of each of the components individually and in a complete case study example of the full pipeline are presented as the study prepares to move toward demonstration on the International Space Station.
日益增多的太空碎片使太空领域越来越接近级联式凯斯勒综合征,这是一种碎片产生的连锁反应,可能会严重阻碍太空的实际利用。与此同时,越来越多的退役卫星,特别是在地球静止轨道等高轨道上的卫星,除了一些小但关键的故障或燃料耗尽外,几乎仍能正常运行。维修这些出现故障的卫星和清理“高价值”太空碎片仍然是一项艰巨的挑战,但通过各种交会演示任务可以看出,利用自主修复和脱轨航天器对这些目标进行主动拦截正逐渐成为现实。然而,一些实际挑战仍未得到解决和验证。由于缺乏轨道保持能力,太空碎片和燃料耗尽的卫星经常在轨道上不受控制地翻滚。为了进行在轨维修或主动清除碎片,对接航天器(“追踪器”)必须考虑这些目标(“目标物”)的翻滚运动,而这种运动往往是未知的。考虑到目标物的翻滚动力学,追踪航天器必须有一种算法方法来识别目标物的翻滚状态,然后利用这些信息进行有用的运动规划和控制。此外,必须仔细考虑任何机动动作固有的不确定性,以便为系统性能提供保障。本研究提出了与这样一个目标物交会的完整流程,从远距离估计点到固定在旋转目标物体坐标系中的对接点。应用一种新颖的视觉估计算法,使用三维飞行时间相机对目标物的旋转状态及其旋转主轴进行远程远距离估计。采用了一种新颖的运动规划算法,利用潜在目标物翻滚类型的离线模拟生成一个查找表,该查找表在轨道上使用估计数据进行解析。这种基于非线性规划的算法考虑了已知的目标物几何形状和重要的实际约束条件,如视野要求,在目标物的旋转体坐标系中生成运动计划。同时,展示了一种不确定性表征方法,该方法将目标物翻滚不确定性中的不确定性进行传播,以在惯性坐标系中为运动计划的参考轨迹提供干扰边界。最后,将这个不确定性边界提供给一个鲁棒管模型预测控制器,该控制器为系统沿参考轨迹平移跟踪的能力提供基于管的鲁棒性保障。展示了这些方法的组合和接口,并额外讨论了它们在NASA的Astrobee自由飞行器计划演示中的一些实际应用。随着该研究准备在国际空间站上进行演示,分别给出了每个组件的仿真结果以及完整流程的完整案例研究示例。