通过视觉无人机队伍进行协作目标搜索:一个自适应的课程嵌入式多阶段增强学习方法
IEEE transactions on neural networks and learning systems
|November 20, 2023
概括
本研究介绍了视觉无人机群体的自适应课程嵌入式多阶段学习 (ACEMSL),以有效地执行协作目标搜索 (CTS). 这种新的方法使得数据高效的培训和成功的现实世界部署复杂的搜索任务.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 使用视觉无人机群的协作目标搜索 (CTS) 对于灾难救援和物流至关重要.
- 挑战包括3D稀疏奖励探索,有限的视觉感知和复杂的协作行为.
- 现有的方法在动态环境中的数据效率和适应性方面扎.
研究的目的:
- 为视觉无人机群中CTS开发一个数据效率高的深度强化学习 (DRL) 方法.
- 为了应对稀疏的奖励,有限的感知和多代理合作的挑战.
- 在现实场景中实现有效的CTS自主操作.
主要方法:
- 为视觉无人机群体提供嵌入式多阶段学习 (ACEMSL) 的拟议适应性课程.
- 将CTS分解为子任务:避开障碍,搜索目标和代理之间的协作.
- 实施了自适应式嵌入式课程 (AEC),以根据成功率调整任务难度.
主要成果:
- ACEMSL展示了数据有效的培训和有效的个人-团队奖励分配.
- 这种方法在没有微调的情况下成功地部署在CTS的真实无人机群中.
- 广泛的模拟和现实世界的测试验证了该方法的有效性和通用性.
结论:
- 在视觉无人机群中,ACEMSL为数据效率高的协作目标搜索提供了有效的解决方案.
- 该方法解决了稀疏奖励探索和多代理协调的关键挑战.
- 在模拟和真实世界飞行测试中验证了有效性,为实际应用铺平了道路.
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