Identification, control and visually-guided behavior for a model helicopter

Unrestricted Research on unmanned aerial vehicles is motivated by applications where human intervention is impossible, risky or expensive e.g. hazardous material recovery, traffic monitoring, disaster relief support, military operations etc. Due to its vertical take-off, landing and hover capabilities, a helicopter is an attractive platform for such applications. There are significant challenges to building an autonomous robotic helicopter -- these span the areas of system identification, low-level control, state estimation, and planning.; Towards the goal of fully-autonomous helicopters this thesis makes the following contributions. A continuous-discrete extended Kalman filter has been developed that combines inertial data with GPS and compass data to provide estimates of the 6DOF state of the helicopter. Using this filter a model for the helicopter has been identified based on frequency response techniques. The model has been validated in flight tests on a small helicopter testbed (1.6 m rotor diameter) at speeds upto 5 m/s. Based on evidence from this model a decoupled low-level controller has been developed which is embedded in a control architecture suitable for visually-guided navigation. As a novel application, we show how such a controller can be used to perform trajectory following on the helicopter where the desired trajectories are typical spacecraft landing trajectories, and the only controls available are thrusters. This in effect, produces a low-cost testbed for testing spacecraft landing and hazard avoidance on a planetary surface. -- Finally, we develop and extensively experimentally characterize algorithms for vision-based autonomous landing, object tracking, and sensor deployment.

针对无人飞行器（unmanned aerial vehicles, UAV）的自由研究，其动机源自人类无法介入、存在安全隐患或成本高昂的应用场景，例如危险物质回收、交通监测、救灾支援、军事行动等。直升机凭借垂直起降与悬停能力，成为此类应用的极具吸引力的平台。研发自主旋翼直升机存在诸多显著挑战，涵盖系统辨识、底层控制、状态估计与路径规划等领域。为实现全自主直升机的研发目标，本论文作出如下贡献：本研究开发了一种连续离散扩展卡尔曼滤波器（continuous-discrete extended Kalman filter），该滤波器融合惯性数据、GPS与罗盘数据，以估计直升机的六自由度（six degrees of freedom, 6DOF）状态。借助该滤波器，研究人员基于频域响应技术完成了直升机动力学模型的辨识。该模型已在旋翼直径1.6米的小型直升机试验平台上完成飞行验证，测试速度最高可达5米/秒。基于该模型的辨识结果，研究人员开发了解耦式底层控制器，并将其嵌入适配视觉导航的控制架构中。作为一项创新性应用，本研究展示了如何将该控制器应用于直升机轨迹跟踪任务：此时期望轨迹采用典型航天器着陆轨迹，且仅可使用推进器作为控制执行机构。该方案实际上构建了一套低成本试验平台，可用于行星表面航天器着陆与避障相关测试。最后，本研究开发了基于视觉的自主着陆、目标跟踪与传感器部署算法，并通过大量实验对这些算法进行了性能表征。