多无人机协同感知增强路径跟踪与避障，实现高效安全飞行。
Cooperative Sensing Enhanced UAV Path-Following and Obstacle Avoidance with Variable Formation
- 基于深度强化学习构建高精度编队路径跟踪模型。
- 利用ISAC信号融合提升障碍物定位精度，降低估计误差。
- 无需预训练的在线避障方案，适合动态环境应用。
无人机的高机动性使其在救援、货运等民用领域广泛应用。路径跟踪是完成任务的关键，而感知与避障对飞行安全至关重要。本文研究如何高效准确地实现路径跟踪、障碍物感知与避障子任务，并解决其无冲突融合调度问题。首先，提出一种基于深度强化学习（DRL）的高精度无人机编队路径跟踪模型，设计了考虑距离与速度误差的自适应权重奖励函数。其次，利用集成感知与通信（ISAC）信号检测障碍物，基于信息级融合推导障碍物感知的克拉美-罗下界（CRLB），并提出可变编队增强的障碍物位置估计算法（VFEO）。此外，设计了一种无需预训练的在线避障方案，以应对稀疏奖励问题。最后，借助基于零空间（NSB）的行为方法，提出分层子任务融合策略。仿真结果验证了各子任务算法及分层融合策略的有效性与优越性。
原文摘要 · Abstract (English)
The high mobility of unmanned aerial vehicles (UAVs) enables them to be used in various civilian fields, such as rescue and cargo transport. Path-following is a crucial way to perform these tasks while sensing and collision avoidance are essential for safe flight. In this paper, we investigate how to efficiently and accurately achieve path-following, obstacle sensing and avoidance subtasks, as well as their conflict-free fusion scheduling. Firstly, a high precision deep reinforcement learning (DRL)-based UAV formation path-following model is developed, and the reward function with adaptive weights is designed from the perspective of distance and velocity errors. Then, we use integrated sensing and communication (ISAC) signals to detect the obstacle and derive the Cramer-Rao lower bound (CRLB) for obstacle sensing by information-level fusion, based on which we propose the variable formation enhanced obstacle position estimation (VFEO) algorithm. In addition, an online obstacle avoidance scheme without pretraining is designed to solve the sparse reward. Finally, with the aid of null space based (NSB) behavioral method, we present a hierarchical subtasks fusion strategy. Simulation results demonstrate the effectiveness and superiority of the subtask algorithms and the hierarchical fusion strategy.
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