复杂结构近距离视觉检测用微四旋翼无人机室内测试与仿真平台
Indoor Testing and Simulation Platform for Close-distance Visual Inspection of Complex Structures using Micro Quadrotor UAV
AI总结：
本文提出了一种基于微四旋翼无人机的低成本实验平台，用于测试无人机路径规划算法，通过室内环境实现高效、安全的近距离视觉检测，验证了现有路径规划算法在实际应用中的有效性。
摘要：
近年来，使用无人机（也称为无人驾驶飞行器，UAV）进行近距离视觉检测已成为多个学科中的活跃研究领域。然而，在实现自主检测之前，仍有许多挑战需要克服，尤其是在检测复杂结构时。复杂的民用结构，如桥梁、水坝和风力涡轮机，规模庞大且几何复杂。这要求使用复杂的路径规划算法来实现近距离检测，同时避免碰撞。在实践中，直接在这些结构上部署路径规划结果容易出错、成本高且充满危险。本文基于微四旋翼无人机，提出了一种经济实惠的实验平台，用于测试基于无人机的路径规划结果。该平台允许用户随时进行多种路径规划实验，而无需担心昂贵且耗时的户外飞行测试。该平台基于Crazyflie套件开发，包括Crazyflie 2.0四旋翼、Crazyradio和定位系统（LPS）。该平台配备 onboard 微型FPV相机，飞行过程中可实时流式传输视觉数据到主机计算机。该平台明确设计了手动配置和航点控制功能，以提高其在路径跟随和调试方面的灵活性和性能。为了评估所提出测试平台的实用性，测试了两种现有的基于无人机的路径规划算法。结果表明，尽管存在一定程度的误差，视觉数据的质量和路径跟随的准确性足以模拟大多数实际检测应用。
英文摘要：
In recent years, using drone, also known as unmanned aerial vehicle (UAV), in close-distance visual inspection has became an active area in many disciplines. However, many challenges still remain before we can achieve autonomous inspection, especially when inspecting complex structures. The complex civil structures, such as bridges, dams and wind turbines, are large-scale and geometrical complicated. It requires sophisticated path planning algorithms to achieve close-distance inspection and, at the same time, avoid collisions. In practice, directly deploying the path planning result on such structures is error prone, costly, and full of hazards. In this paper, rely on micro quadrotor UAV, the authors present an affordable experimental platform for testing drone-based path planning result. The platform allows the users to conduct many path planning experiments at any time without worrying expensive and time consuming outdoor test flying. This platform is developed based on the bundle of Crazyflie, which includes Crazyflie 2.0 quadrotor, Crazyradio and loco positioning system (LPS). Equipped with an onboard micro FPV camera, the visual data can be lively streamed to the host computer during flight. The functions of manual configuration and waypoints control are explicitly designed in this platform to increase its flexibility and performance on path following and debugging. To evaluate the practicability of the proposed test platform, two existing drone-based path planning algorithms are tested. The results show that even though certain level of error existed, the quality of visual data and accuracy of path following are high enough for simulating most practical inspection applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
