Cooperative Sensing Enhanced UAV Path-Following and Obstacle Avoidance with Variable Formation

--The high mobility of unmanned aerial vehicles (UA Vs) 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 UA V 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 Cram er-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. Index T erms --UA V formation, path-following, cooperative sensing, obstacle avoidance, hierarchical subtasks fusion, integrated sensing and communication. ITH the development of wireless communication and intelligent control, unmanned aerial vehicle (UA V) plays an important role in the civilian field. However, the detection and communication capabilities of a single UA V are limited. Multiple UA Vs provide enhanced coverage and stability [1], enabling them to perform various tasks such as rescue, cargo transport, emergency communications, and area search [2].