视觉目标驱动的机器人人群导航具有有限的FOV 使用自我注意力增强深度强化学习学习
Yinbei Li1, Qingyang Lyu2, Jiaqiang Yang1
1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种新的视觉导航系统,用于使用深度强化学习 (DRL) 和自我注意的移动机器人. 该方法增强了动态环境中的目标跟踪和避难障碍,提高了机器人导航成功率.
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:
- 同时定位和映射 (SLAM) 方法在动态,不可预测的环境中面临挑战.
- 在拥挤的空间中移动机器人导航需要强大的避障能力和目标追逐能力.
研究的目的:
- 为使用自我注意力增强深度强化学习 (DRL) 的移动机器人开发一种视觉目标驱动的导航方法.
- 在动态环境中克服传统SLAM的局限性,提高机器人导航效率.
主要方法:
- 利用双延迟深度决定性政策梯度 (TD3) 算法用于导航政策开发.
- 使用单个RGB-D摄像头和卷积神经网络 (CNN) 来提取环境特征.
- 集成了一个自我注意网络 (SAN) 来弥补视野有限 (FOV) 和帮助目标重新获取.
主要成果:
- 与传统方法相比,在动态环境中取得更高的成功率.
- 证明了更短的平均目标达到时间.
- 验证了该方法在增强具有有限FOV的目标搜索方面的有效性.
结论:
- 拟议的自我注意力增强的DRL方法为移动机器人在充满挑战的动态环境中进行导航提供了强大的解决方案.
- 该系统提供硬件简单性,成本效益和现实世界部署的方便性.
- 这种方法显著提高了机器人有效地导航和到达目标的能力,即使在暂时丢失时也是如此.
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