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使用基于深度学习的细分方法用于室内导航的差分驱动的移动机器人可驱动路径检测
1Department of Mechatronics Engineering, Bursa Technical University, Bursa, Türkiye.
PeerJ. Computer science
|December 16, 2024
概括
这项研究介绍了移动机器人的深度学习方法,以检测室内安全路径. 该方法使用语义细分和基于网格的RRT*策略来实现高效和最佳的导航,增强机器人的智能.
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:
- 深度学习增强了机器人的智能,并实现了有意义的任务执行.
- 移动机器人需要用于复杂环境的智能导航系统.
- 在杂乱的室内空间中寻找路径仍然是自主机器人面临的重大挑战.
研究的目的:
- 开发一种基于语义细分的方法,用于检测室内移动机器人导航的可驾驶路径.
- 提高移动机器人路径规划在障碍丰富的环境中的安全性和最佳性.
- 将深度学习与路径查找算法集成为增强的自主导航.
主要方法:
- 使用DeepLabv3+与ResNet50骨干用于准确的可驱动路径检测的新语义细分方法.
- 透视转换将分段图像映射到现实世界的基于网格的运动空间.
- 一个基于网格的快速探索随机树星 (RRT*) 导航策略,带有路径平滑,以最佳地避开障碍.
主要成果:
- 拟议的方法准确地确定可驾驶路径,使移动机器人能够以最佳方式到达目标.
- 与其他方法相比,DeepLabv3+和ResNet50表现出优越的细分性能 (0.21% - 4.18%的改进).
- 集成系统成功导航了各种室内场景,验证了可驾驶路径确定方法.
结论:
- 语义细分与基于网格的RRT*战略相结合,为室内移动机器人导航提供了有效的解决方案.
- 开发的方法通过允许安全和最佳的路径寻找来增强机器人的智能和自主性.
- 该研究通过现实世界移动机器人测试来验证拟议方法的实际适用性.
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