协同感知增强的可变编队无人机路径跟踪与避障
Cooperative Sensing Enhanced UAV Path-Following and Obstacle Avoidance with Variable Formation
AI总结：
针对无人机编队安全执行任务的需求，本文结合DRL路径跟踪、ISAC协同感知、VFEO定位、在线避障与NSB分层融合，实现无冲突的路径跟踪和障碍物规避。
摘要：
无人机（UAV）的高机动性使其能够应用于救援和货物运输等各种民用领域。路径跟踪是执行这些任务的关键方式，而感知与避障则是安全飞行的必要条件。本文研究如何高效且准确地完成路径跟踪、障碍物感知与避障子任务，以及它们之间无冲突的融合调度。首先，建立了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，并从距离误差和速度误差的角度设计了具有自适应权重的奖励函数。随后，利用通感一体化（ISAC）信号检测障碍物，并通过信息级融合推导障碍物感知的克拉美罗下界（CRLB），在此基础上提出可变编队增强障碍物位置估计（VFEO）算法。此外，设计了一种无需预训练的在线避障方案，以解决稀疏奖励问题。最后，借助基于零空间（NSB）的行为方法，提出了一种分层子任务融合策略。仿真结果证明了各子任务算法及分层融合策略的有效性和优越性。
英文摘要：
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.
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。
