Dynamic environment simulation using indoor drones

无人机（uav）正在成为许多行业的常用数据收集和运输工具，包括农业、石油和天然气、保险、搜索和救援、基础设施、房地产和电影行业。在每个应用中，无人机都需要携带和指向特定的有效载荷（通常是摄像头，但有时也需要其他传感器，如激光雷达或气体传感器），以便收集所需的数据。相对简单的反馈控制已被用于控制无人机，但随着使用案例和潜在有效载荷的数量逐年增长，仅具有无人机动力学基本知识的简单反馈控制器的鲁棒性受到质疑。MicroPilot需要使用商用自动驾驶硬件和软件识别特定车辆动力学的技术，以便将前馈控制项包含在自动驾驶控制器中，以更好地补偿未知和未建模的无人机动力学。该研究项目将开发和验证一套无人机控制和估计算法，以识别无人机动态并以自适应方式补偿它们。有了这项创新技术，MicroPilot的自动驾驶仪产品将能够适应和补偿各种各样的无人机和有效载荷，而无需重新编程。这一进步将使MicroPilot公司的产品更适用于新兴行业和用例，从而使其在全球无人机市场上更具竞争力。

Unmanned Aerial Vehicles (UAVs) are becoming a common data collection and transport tool for many industries, including agriculture, oil and gas, insurance, search and rescue, infrastructure, real estate and the film industry. In each application, UAVs are required to carry and point specific payloads (usually cameras, but sometimes other sensors such as LIDARs or gas sensors) in order to gather the desired data. Relatively simple feedback control has been used to control UAVs, but with the number of use cases and potential payloads growing each year, the robustness of simple feedback controllers with only rudimentary knowledge of the UAV dynamics is in question. MicroPilot needs techniques for identifying specific vehicle dynamics using commercial autopilot hardware and software so that feed-forward control terms can be included in the autopilot controller to better compensate for unknown and un-modelled UAV dynamics. This research project will develop and validate a set of UAV control and estimation algorithms that identify the UAV dynamics and compensate for them in an adaptive sense. Armed with this innovative technology, MicroPilot's autopilot products will be able to adapt to and compensate for a wide variety of UAVs and payloads without requiring re-programming. This advancement will make MicroPilot more competitive in the global UAV market by making their product more applicable to new industries and use cases as they arise.