用硬件在环测试飞行动作，提升微型飞行机器人状态估计精度。
Hardware-in-the-Loop for Characterization of Embedded State Estimation for Flying Microrobots
- 构建硬件在环系统模拟哈佛蜂鸟机器人飞行轨迹与振动噪声。
- 在100mg微型飞行器上验证了互补扩展卡尔曼滤波的稳定姿态估计算法。
- 适合研究微型飞行器自主控制与机载传感系统的研究人员参考。
自主扑翼微型飞行器（FWMAV）在环境监测、人工授粉和搜救等领域具有广泛应用前景。然而，受限于体积小、载荷能力弱，其机载传感器套件的部署面临挑战。当前高精度状态估计依赖外部运动捕捉摄像头，导致飞行仅限于特定航域。此外，微小载荷与高度非线性振荡动力学使机载传感器的状态估计困难，受算力不足与传感器噪声影响。本文提出一种新型硬件在环（HWIL）测试流程，复现哈佛蜂鸟机器人（100mg FWMAV）的飞行轨迹。通过该流程评估多种传感器组合在鲁棒高度与姿态估计中的表现，采用互补扩展卡尔曼滤波实现状态估计。系统包含机械噪声生成器，可同步模拟真实飞行中的轨迹与振动。本方案为实现微型飞行器完全自主控制提供了关键技术支撑。
原文摘要 · Abstract (English)
Autonomous flapping-wing micro-aerial vehicles (FWMAV) have a host of potential applications such as environmental monitoring, artificial pollination, and search and rescue operations. One of the challenges for achieving these applications is the implementation of an onboard sensor suite due to the small size and limited payload capacity of FWMAVs. The current solution for accurate state estimation is the use of offboard motion capture cameras, thus restricting vehicle operation to a special flight arena. In addition, the small payload capacity and highly non-linear oscillating dynamics of FWMAVs makes state estimation using onboard sensors challenging due to limited compute power and sensor noise. In this paper, we develop a novel hardware-in-the-loop (HWIL) testing pipeline that recreates flight trajectories of the Harvard RoboBee, a 100mg FWMAV. We apply this testing pipeline to evaluate a potential suite of sensors for robust altitude and attitude estimation by implementing and characterizing a Complimentary Extended Kalman Filter. The HWIL system includes a mechanical noise generator, such that both trajectories and oscillatinos can be emulated and evaluated. Our onboard sensing package works towards the future goal of enabling fully autonomous control for micro-aerial vehicles.
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