基于逆PPO的固定翼无人机单目避障
Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs
AI总结：
针对传统固定翼无人机避障系统在未知环境和小型平台的局限性，本文提出基于逆PPO的轻量级DRL避障系统，通过单帧图像深度推理模块和自适应熵调整机制，实现仅用视觉传感器以超30m/s速度避障，性能优于其他方法且可在边缘设备部署。
摘要：
固定翼无人机（Fixed-wing Unmanned Aerial Vehicles, UAVs）凭借其长续航和高速能力，是新兴低空经济（Low-altitude Economy, LAE）与城市空中交通（Urban Air Mobility, UAM）中最常用的平台之一。依赖先验地图或复杂传感器的传统避障系统，在未知低空环境和小型无人机平台中存在局限性。对此，本文提出一种轻量级的基于深度强化学习（deep reinforcement learning, DRL）的无人机避障系统，仅使用机载视觉传感器，就能使固定翼无人机以超过30m/s的巡航速度避开未知障碍物。该系统采用具有精简网络架构的单帧图像深度推理模块，确保实时障碍物检测，且针对边缘计算设备进行了优化。之后，设计了带有新型奖励函数的强化学习控制器，以平衡目标接近度和飞行轨迹平滑度，满足固定翼无人机平台特定的动态约束和稳定性要求。引入自适应熵调整机制，缓解DRL固有的探索-利用权衡问题，提高训练收敛性和避障成功率。大量的软件在环和硬件在环实验表明，所提出的框架在避障效率和飞行轨迹平滑度方面优于其他方法，并证实了该算法在边缘设备上实现的可行性。源代码公开于\ud83d\udc49https://github.com/ch9397/FixedWing-MonoPPO。
英文摘要：
Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the burgeoning Low-altitude Economy (LAE) and Urban Air Mobility (UAM), due to their long endurance and high-speed capabilities. Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environments and small UAV platforms. In response, this paper proposes a lightweight deep reinforcement learning (DRL) based UAV collision avoidance system that enables a fixed-wing UAV to avoid unknown obstacles at cruise speed over 30m/s, with only onboard visual sensors. The proposed system employs a single-frame image depth inference module with a streamlined network architecture to ensure real-time obstacle detection, optimized for edge computing devices. After that, a reinforcement learning controller with a novel reward function is designed to balance the target approach and flight trajectory smoothness, satisfying the specific dynamic constraints and stability requirements of a fixed-wing UAV platform. An adaptive entropy adjustment mechanism is introduced to mitigate the exploration-exploitation trade-off inherent in DRL, improving training convergence and obstacle avoidance success rates. Extensive software-in-the-loop and hardware-in-the-loop experiments demonstrate that the proposed framework outperforms other methods in obstacle avoidance efficiency and flight trajectory smoothness and confirm the feasibility of implementing the algorithm on edge devices. The source code is publicly available at \url{https://github.com/ch9397/FixedWing-MonoPPO}.
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。
