A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis
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DOI：
10.1109/access.2021.3128866
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Zhenyu Zhou;Yanchao Liu
中科院分区：
文献类型：
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作者：
Zhenyu Zhou;Yanchao Liu
由于硬件和外形因素的限制，多旋翼无人驾驶飞机(无人机)无法根据自身能力进行飞行前体检。关键的安全检查涉及发现各种异常情况，如有效载荷不平衡、螺旋桨损坏、发动机失灵和罗盘校准不当等。目前需要人力来执行这些任务，这阻碍了无人机的大规模部署，并增加了业务成本。在这项工作中，我们提出了一个重量测量着陆平台以及一套统计推理算法，旨在对降落在该平台上的任何多人飞机进行安全检查。建立了估计飞行器重心和姿态的非凸非线性最小二乘模型，并推导了最优解的递推计算公式。在数值实验中，我们的解析解方法能够比全局优化求解器快几个数量级地找到全局解。我们已经对一架四轴飞行器进行了真实系统测试，该无人机被故意配置为携带错误的有效载荷，并使用损坏的螺旋桨。实验结果表明，该平台能够以较高的准确率检测和剖析这些常见的安全问题。
Due to the limitation imposed by hardware and form factor considerations, multirotor unmanned aircraft (drones) are unable to conduct preflight physical checks on their own capacity. Critical safety checks involve detecting various anomalies such as imbalanced payload, damaged propellers, mulfunctioning motors and poorly calibrated compass, etc. Human efforts are currently required for performing such tasks, which impedes large-scale deployments of drones and increases the operational costs. In this work, we propose a weight-measuring landing platform along with a set of statistical inference algorithms aimed at performing safety checks for any multicopter aircraft that lands on the platform. We develop a nonconvex nonlinear least squares model for estimating the center of gravity and orientation of the aircraft, and derive a recursive formula for calculating the optimal solution. In numeric experiments, our analytical solution method has been able to find the global solution orders-of-magnitude faster than a global optimization solver. We have conducted real-system tests on a quadcopter drone deliberately configured to carry misplaced payload, and to use damaged propellers. Experiment results show that the platform is able to detect and profile these common safety issues with high accuracy.