TornadoDrone：基于生物启发DRL的无人机在风力扰动下于6D平台着陆
TornadoDrone: Bio-inspired DRL-based Drone Landing on 6D Platform with Wind Force Disturbances
AI总结：
提出基于生物启发DRL的TornadoDrone模型，利用位置和速度等间接线索适应风力，在6D移动平台上实现高精度着陆，性能超越传统PID与扩展卡尔曼滤波方法。
摘要：
自主无人机导航在动态平台上实现精确着陆面临关键挑战，尤其是在风湍流等不可预测条件下。我们的研究引入了TornadoDrone，这是一种新颖的深度强化学习（DRL）模型，采用生物启发机制来适应风力，模仿鸟类在自然界中的适应能力。与传统方法不同，该模型的适应能力源于位置和速度变化等间接线索，而非直接的风力测量。TornadoDrone在gym-pybullet-drone模拟器中进行了严格训练，该模拟器高度还原了现实世界中风力动力学的复杂性。通过在模拟和现实多风环境中对Crazyflie 2.1无人机进行广泛测试，TornadoDrone在移动平台上保持高精度着陆方面表现出色，超越了结合扩展卡尔曼滤波器的PID控制器等传统控制方法。该研究不仅突显了DRL应对复杂空气动力学挑战的潜力，也为能够实时适应环境变化的先进自主系统铺平了道路。TornadoDrone的成功标志着无人机技术的飞跃，特别是在可靠性和精度至关重要的监控和应急响应等关键应用中。
英文摘要：
Autonomous drone navigation faces a critical challenge in achieving accurate landings on dynamic platforms, especially under unpredictable conditions such as wind turbulence. Our research introduces TornadoDrone, a novel Deep Reinforcement Learning (DRL) model that adopts bio-inspired mechanisms to adapt to wind forces, mirroring the natural adaptability seen in birds. This model, unlike traditional approaches, derives its adaptability from indirect cues such as changes in position and velocity, rather than direct wind force measurements. TornadoDrone was rigorously trained in the gym-pybullet-drone simulator, which closely replicates the complexities of wind dynamics in the real world. Through extensive testing with Crazyflie 2.1 drones in both simulated and real windy conditions, TornadoDrone demonstrated a high performance in maintaining high-precision landing accuracy on moving platforms, surpassing conventional control methods such as PID controllers with Extended Kalman Filters. The study not only highlights the potential of DRL to tackle complex aerodynamic challenges but also paves the way for advanced autonomous systems that can adapt to environmental changes in real-time. The success of TornadoDrone signifies a leap forward in drone technology, particularly for critical applications such as surveillance and emergency response, where reliability and precision are paramount.
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。
