FLAGRED -- 基于模糊逻辑的无人机风险估计泛化算法
FLAGRED -- Fuzzy Logic-based Algorithm Generalizing Risk Estimation for Drones
AI总结：
本文提出一种基于模糊逻辑的无人机风险实时估计算法FLAGRED，仅利用电机指令数据即可泛化到不同机型，实测性能优于ArduCopter并有效区分高低风险场景。
摘要：
实时准确估计风险对于确保涉及自主机器人系统的许多应用的安全性和效率至关重要。本文提出了一种新颖、可泛化的算法，用于实时估计外部扰动对多旋翼飞行器造成的风险。与传统方法不同，我们的方法不需要额外的传感器、精确的无人机模型或大型数据集。它在模糊逻辑系统中使用电机指令数据，克服了实际部署的障碍。该算法具有固有的适应性，利用无人机的基本特性，使其适用于各种无人机模型。该算法的有效性已通过在多种平台上的全面实际测试得到证实。它能够熟练地区分由不同风力扰动和不同推力重量比导致的高风险和低风险场景。该算法的性能超过了广受认可的ArduCopter风力估计算法，并展示了其及时检测短暂阵风的能力。
英文摘要：
Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conventional approaches, our method requires no additional sensors, accurate drone models, or large datasets. It employs motor command data in a fuzzy logic system, overcoming barriers to real-world implementation. Inherently adaptable, it utilizes fundamental drone characteristics, making it applicable to diverse drone models. The efficiency of the algorithm has been confirmed through comprehensive real-world testing on various platforms. It proficiently discerned between high and low-risk scenarios resulting from diverse wind disturbances and varying thrust-to-weight ratios. The algorithm surpassed the widely-recognized ArduCopter wind estimation algorithm in performance and demonstrated its capability to promptly detect brief gusts.
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。
