Fault-Tolernat Vision-Guided Robotic Systems for Aerospace Applications

出于科学、商业和战略的原因，以加拿大标志性的Canadarm技术为代表的空间机器人技术对加拿大的国家利益具有重要意义。特别是，可靠的视觉导航系统对于许多当前和不久的未来机器人空间任务中的自主操作至关重要，以支持交会、近距离机器人操作和自动行星着陆。对加拿大来说，由于其广阔的国内领空，为各种民用用途采用配备视觉导航系统的空中机器人车辆也很重要。尽管在过去的二十年里取得了很大的进展，但视觉导航机器人系统仍然面临着许多具有挑战性的问题，主要是由于视觉系统的不可靠性、环境的不确定性以及实际GN&C的多重物理和操作限制。在航空航天应用中测试视觉导航机器人系统尤其具有挑战性，因为测试通常应该在实验室环境中进行，而最终的视觉导航机器人系统在航空航天环境中运行。这项研究计划旨在通过在故障检测和恢复、自适应监控以及可扩展的硬件在环仿真测试方法方面的一系列进步来增强视觉导航机器人系统的稳健性。这种方法足够普遍，适用于太空和空中机器人系统。最终目标是开发一种航空航天机器人系统电缆，不仅针对不准确和潜在的错误视觉信息，而且针对影响飞行动力学和系统性能的动力学和校准不确定性进行自适应调整。当视觉系统发生部分或全部故障时，自适应监督控制选择最合适的控制动作。本研究还旨在开发一种可扩展的半实物仿真系统，用于视觉导航机器人系统的真实感测试。为视觉算法和GN&C提供实时3D视觉数据是由实际的激光测距仪或立体相机产生的，而近距离操作或行星着陆时的相对运动是由模拟机器人根据飞行动力学产生的。将采用一种类似于流体力学/量纲分析的无量纲数学技术，对模型几何形状和模拟状态(如距离和速度)进行适当的缩放，以便在面对实验室测试的物理限制时实现动态相似。*参与这项研究的研究生将有一个独特的机会获得航空航天工程的实用技能，同时致力于先进的GN&C、自适应估计、3D视觉系统、故障恢复策略和半实物仿真技术理论发展的前沿问题。

Space robotics manifested by the iconic Canadarm technology is important to Canadian national interest for scientific, commercial, and strategic reasons. In particular, reliable vision-guided systems are critical for autonomous operation in many current and near future robotics space missions to support rendezvous, proximity robotic operations, and automated planetary landing. Adoption of aerial robotic vehicles equipped with vision-guided system for a variety of civilian uses is also important for Canada due to the vastness of its domestic airspace. Despite of significant progress made in the past two decades, vision guided robotic systems still face many challenging problems mainly due to undependability of vision systems, environmental uncertainties, and practical GN&C given multiple physical and operational constraints. Testing vision-guided robotics systems in aerospace applications is particularly challenging because the tests often should perform in a laboratory environment whereas the eventual vision-guided robotic systems operate in an aerospace environment.******This research program is aimed at enhancing the robustness of vision-guided robotic system through a series of advancements in fault detection and recovery, adaptive supervisory control, as well as scalable hardware-in-the-loop simulation test methods. The methodology is general enough for applying to space and aerial robotic systems alike. The ultimate goal is to develop an aerospace robotic system cable of adaptively tuning itself against not only inaccurate and potentially erroneous visual information but against the dynamics and calibration uncertainties which affect the flight dynamics and system performance. The adaptive supervisory control chooses the most appropriate control action if partial or complete failure of the vision system happen. Development of a scalable hardware-in-the-loop simulation for realistic testing of vision-guided robotic systems is also aimed at in this research. Real-time 3D vision data to feed the vision algorithm and GN&C are generated by an actual laser range finder or a stereo camera, while the relative motion in a proximity operation or planetary landing is generated by a simulating robot according to flight dynamics. A dimensionless mathematical technique, analogous to fluid mechanics/dimensional analysis, will be adopted for proper scaling of the mockup geometry and the simulated states such as range and velocities in order to achieve dynamic similarities in the face of physical limitations of the laboratory testing.******The graduate students involved in this proposed research will have a unique opportunity to acquire practical skills in aerospace engineering, while working on problems at the cutting edge of the theoretical development of advanced GN&C, adaptive estimation, 3D vision systems, fault recovery strategies, and HIL simulation technology.