arXiv 2409.039302024-12-23cs.RO

DRAL：用于未知室内环境中多无人机导航的深度强化自适应学习

DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment

University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Irvine（加利福尼亚大学欧文分校）；George Washington University（乔治·华盛顿大学）；Beijing Sineva Robot Technology Co., Ltd（北京思知机器人科技有限公司）；University of Southern California（南加利福尼亚大学）；Santa Clara University（圣克拉拉大学）；Mangosuthu University of Technology（曼戈苏图科技大学）

Kangtong Mo, Linyue Chu, Xingyu Zhang, Xiran Su, Yang Qian, Yining Ou, Wian Pretorius

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出DRAL算法，利用单摄像头和深度强化学习实现无人机室内自主导航与多机协作举物，提升了复杂多智能体系统的鲁棒性与灵活性。

摘要：

无人机（UAV）的自主室内导航面临诸多挑战，主要原因是GPS在封闭环境中的精度有限。此外，无人机携带重型或高功耗传感器（如超重包裹）的能力有限，加剧了在室内实现自主导航的难度。本文提出了一种先进的系统，其中无人机仅使用单个摄像头自主在室内空间导航以定位特定目标，例如未知的Amazon包裹。采用深度学习方法，训练了一种深度强化自适应学习算法，以开发出模拟专家飞行员决策过程的控制策略。我们通过在各种室内环境中进行的实时仿真验证了系统的有效性。我们应用多种可视化技术来更深入地了解我们训练的网络。此外，我们将该方法扩展为包含一种自适应控制算法，用于协调多个无人机在室内环境中协作举起物体。集成我们的DRAL算法使多个UAV能够学习适应动态条件和不确定性的最优控制策略。这一创新增强了室内导航的鲁棒性和灵活性，并为在狭小空间内进行复杂的多无人机操作开辟了新的可能性。所提出的框架突出了在自适应控制和深度强化学习方面的重要进展，为现实世界应用中的复杂多智能体系统提供了鲁棒的解决方案。

英文摘要：

Autonomous indoor navigation of UAVs presents numerous challenges, primarily due to the limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to carry heavy or power-intensive sensors, such as overheight packages, exacerbates the difficulty of achieving autonomous navigation indoors. This paper introduces an advanced system in which a drone autonomously navigates indoor spaces to locate a specific target, such as an unknown Amazon package, using only a single camera. Employing a deep learning approach, a deep reinforcement adaptive learning algorithm is trained to develop a control strategy that emulates the decision-making process of an expert pilot. We demonstrate the efficacy of our system through real-time simulations conducted in various indoor settings. We apply multiple visualization techniques to gain deeper insights into our trained network. Furthermore, we extend our approach to include an adaptive control algorithm for coordinating multiple drones to lift an object in an indoor environment collaboratively. Integrating our DRAL algorithm enables multiple UAVs to learn optimal control strategies that adapt to dynamic conditions and uncertainties. This innovation enhances the robustness and flexibility of indoor navigation and opens new possibilities for complex multi-drone operations in confined spaces. The proposed framework highlights significant advancements in adaptive control and deep reinforcement learning, offering robust solutions for complex multi-agent systems in real-world applications.

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。