小型多旋翼无人机航空遥感平台机载作业自主定位控制系统设计

Design of Autonomous Positioning Control System for Airborne Operations of Small Multi rotor Unmanned Aerial Vehicle Aerial Remote Sensing Platform

摘要:

针对小型多旋翼无人机在航空遥感作业中面临的复杂环境适应性差、自主定位精度低及多传感器协同控制效率不足等问题，设计小型多旋翼无人机航空遥感平台机载作业自主定位控制系统。在机载作业控制模块中，集成主控硬件、惯性测量单元硬件、多种传感器硬件、通信硬件以及电源硬件。在此基础之上，设计“激光-视觉-惯性”紧耦合定位模块，利用其实现小型多旋翼无人机航空遥感平台机载作业自主定位。在双闭环比例积分微分（Proportional-Integral-Derivative，PID）控制器模块中，设计双闭环PID控制器，分别利用三个子控制器实现小型多旋翼无人机航空遥感平台机载作业中姿态、位置以及高度的双闭环PID控制。在导航避障模块中，设计融合A*算法与动态窗口法的无人机避障路径规划算法，实现动态避障路径规划。系统测试结果表明，设计系统的动态环境干扰抑制指数（Dynamic Environment Suppression Index，DESI）整体高于0.84，说明设计系统对动态环境的干扰抑制能力强，能在极端条件下保持高稳定性。设计系统的定位结果与实验四旋翼无人机实际位置姿态最接近，说明设计系统的自主定位精度高。设计系统的任务-感知协同增益比（Task-Cognition Synergy Ratio，TCR）整体高于0.8，说明设计系统的多传感器协同控制能够显著提升任务效率，能耗与时间均较低。

Abstract:

Aiming at the problems faced by small multi rotor unmanned aerial vehicles in aerial remote sensing operations, such as poor adaptability to complex environments, low autonomous positioning accuracy, and insufficient efficiency of multi-sensor collaborative control, a small multi rotor unmanned aerial vehicle aerial remote sensing platform airborne autonomous positioning control system is designed. In the onboard operation control module, the main control hardware, inertial measurement unit hardware, various sensor hardware, communication hardware, and power hardware are integrated. On this basis, a "laser vision inertia" tightly coupled positioning module is designed to achieve autonomous positioning of small multi rotor unmanned aerial vehicle aerial remote sensing platform for airborne operations. In the dual loop Proportional Integral Derivative (PID) controller module, a dual loop PID controller is designed to achieve dual loop PID control of attitude, position, and altitude in the airborne operation of a small multi rotor unmanned aerial vehicle remote sensing platform using three sub controllers. In the navigation obstacle avoidance module, design a drone obstacle avoidance path planning algorithm that integrates A * algorithm and dynamic window method to achieve dynamic obstacle avoidance path planning. The system test results show that the overall Dynamic Environment Suppression Index (DESI) of the designed system is higher than 0.84, indicating that the designed system has strong interference suppression ability for dynamic environments and can maintain high stability under extreme conditions. The positioning results of the design system are closest to the actual position and attitude of the experimental quadcopter drone, indicating that the autonomous positioning accuracy of the design system is high. The overall Task Recognition Synergy Ratio (TCR) of the design system is higher than 0.8, indicating that the multi-sensor collaborative control of the design system can significantly improve task efficiency with low energy consumption and time.