具有动态障碍物的三维环境中的自主无人机集群导航与多目标跟踪
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles
AI总结：
针对复杂三维动态环境下无人机集群导航与多目标跟踪难题，提出含动态集群管理、避障寻路及目标跟踪与岛屿建模的三阶段深度强化学习策略，实现集群分裂合并并有效跟踪多类目标。
摘要：
人工集群的自主建模是必要的，因为手动创建是一个耗时且复杂的过程，这使其不切实际。本研究提出了一种采用深度强化学习的自主方法用于集群导航。在该方法中，对具有静态和动态障碍物以及阻力（如线性阻力、角阻力和重力）的复杂三维环境进行建模，以跟踪多个动态目标。此外，还设计了用于鲁棒集群编队和目标跟踪的奖励函数，以学习复杂的集群行为。由于智能体数量不固定且仅具有对环境的局部观测，集群编队和导航变得具有挑战性。对此，所提出的策略包括三个主要阶段以应对上述挑战：1）动态集群管理方法；2）避开障碍物，寻找通往目标的最短路径；3）跟踪目标和岛屿建模。动态集群管理阶段将基本的感官输入转换为高级命令，以增强集群导航和去中心化设置，同时维持集群规模的波动。而在岛屿建模中，集群可以根据目标数量分裂为各个子集群；反过来，这些子集群也可以合并形成一个巨大的集群，从而赋予集群跟踪多个目标的能力。采用定制的最先进基于策略的深度强化学习算法以获得显著结果。令人鼓舞的结果表明，我们提出的策略增强了集群导航，并且能够在复杂动态环境中跟踪多个静态和动态目标。
英文摘要：
Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces (like linear drag, angular drag, and gravity) are modeled to track multiple dynamic targets. Moreover, reward functions for robust swarm formation and target tracking are devised for learning complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of three main phases to tackle the aforementioned challenges: 1) A methodology for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path towards the targets, 3) Tracking the targets and Island modeling. The dynamic swarm management phase translates basic sensory input to high level commands to enhance swarm navigation and decentralized setup while maintaining the swarms size fluctuations. While, in the island modeling, the swarm can split into individual subswarms according to the number of targets, conversely, these subswarms may join to form a single huge swarm, giving the swarm ability to track multiple targets. Customized state of the art policy based deep reinforcement learning algorithms are employed to achieve significant results. The promising results show that our proposed strategy enhances swarm navigation and can track multiple static and dynamic targets in complex dynamic environments.
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。
