视觉无人机集群的协同目标搜索：一种自适应课程嵌入的多阶段强化学习方法
Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach
AI总结：
针对视觉无人机集群在无先验信息下的协同目标搜索难题，提出自适应课程嵌入多阶段强化学习方法，通过任务分解与难度自适应调整实现高效训练，并在真实集群中验证了其有效性与泛化能力。
摘要：
为无人机配备目标搜索能力对于灾难救援和智能仓储配送系统等应用极具价值。多架能够相互协作并在障碍物间机动飞行的智能无人机，在更短时间内完成任务方面展现出更高的效率。然而，在缺乏先验目标信息的情况下开展协同目标搜索（CTS）极具挑战性，对于视觉无人机集群而言尤为如此。在本研究中，我们提出了一种新颖的数据高效的深度强化学习（DRL）方法，称为自适应课程嵌入多阶段学习（ACEMSL），以应对这些挑战，主要包括有限视觉感知下的三维稀疏奖励空间探索以及协作行为要求。具体而言，我们将CTS任务分解为若干子任务，包括个体避障、目标搜索和智能体间协作，并通过多阶段学习逐步训练智能体。同时，设计了自适应嵌入课程（AEC），其中任务难度等级（TDL）可根据训练中取得的成功率（SR）进行自适应调整。ACEMSL能够实现数据高效的训练以及视觉无人机集群的个体-团队奖励分配。此外，我们将训练好的模型部署到真实的视觉无人机集群上，并在无需微调的情况下执行CTS操作。大量仿真和真实世界飞行测试验证了ACEMSL的有效性和泛化能力。项目地址：https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git。
英文摘要：
Equipping drones with target search capabilities is highly desirable for applications in disaster rescue and smart warehouse delivery systems. Multiple intelligent drones that can collaborate with each other and maneuver among obstacles show more effectiveness in accomplishing tasks in a shorter amount of time. However, carrying out collaborative target search (CTS) without prior target information is extremely challenging, especially with a visual drone swarm. In this work, we propose a novel data-efficient deep reinforcement learning (DRL) approach called adaptive curriculum embedded multistage learning (ACEMSL) to address these challenges, mainly 3-D sparse reward space exploration with limited visual perception and collaborative behavior requirements. Specifically, we decompose the CTS task into several subtasks including individual obstacle avoidance, target search, and inter-agent collaboration, and progressively train the agents with multistage learning. Meanwhile, an adaptive embedded curriculum (AEC) is designed, where the task difficulty level (TDL) can be adaptively adjusted based on the success rate (SR) achieved in training. ACEMSL allows data-efficient training and individual-team reward allocation for the visual drone swarm. Furthermore, we deploy the trained model over a real visual drone swarm and perform CTS operations without fine-tuning. Extensive simulations and real-world flight tests validate the effectiveness and generalizability of ACEMSL. The project is available at https://github.com/NTU-UAVG/CTS-visual-drone-swarm.git.
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。
