Scholarlink

The paper proposes a novel visual navigation framework to address the challenges encountered by unmanned aerial vehicles (UAVs) when operating in indoor environments, including insufficient image feature extraction and poor exploration. The framework utilizes depth images, flight state data, and relative orientation information to guide the UAV towards the intended destination in an end-to-end manner. In addition, we have designed a Convolutional Block Attention Module (CBAM) with a non-sparse reward mechanism to enhance the efficiency of visual feature extraction, thereby improving the UAV’s exploratory capabilities. We have deployed the algorithm in an AirSim-based simulation environment, characterized by ever-changing obstacles, to validate its effectiveness. The results demonstrate that our algorithm enables the UAV to avoid obstacles and reach the destination effectively in unfamiliar indoor environments. This solution has significant implications for the effective deployment of UAVs in indoor settings, particularly in applications such as search and rescue, inspection, and surveillance.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Vision-Based Indoor UAV Navigation with Deep Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Yuxuan Guo,
  • Lei Tang,
  • Xin Zhou,
  • Junzhe Zhang,
  • Longbin Chen

摘要

The paper proposes a novel visual navigation framework to address the challenges encountered by unmanned aerial vehicles (UAVs) when operating in indoor environments, including insufficient image feature extraction and poor exploration. The framework utilizes depth images, flight state data, and relative orientation information to guide the UAV towards the intended destination in an end-to-end manner. In addition, we have designed a Convolutional Block Attention Module (CBAM) with a non-sparse reward mechanism to enhance the efficiency of visual feature extraction, thereby improving the UAV’s exploratory capabilities. We have deployed the algorithm in an AirSim-based simulation environment, characterized by ever-changing obstacles, to validate its effectiveness. The results demonstrate that our algorithm enables the UAV to avoid obstacles and reach the destination effectively in unfamiliar indoor environments. This solution has significant implications for the effective deployment of UAVs in indoor settings, particularly in applications such as search and rescue, inspection, and surveillance.