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对农业机器人模拟户外导航进行比较研究
Feeza Khan Khanzada1, Elahe Delavari1, Woojin Jeong2
1Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, MI 48128-2406, USA.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
基于深度神经网络 (DNN) 的行为克隆 (BC) 导航提供了高效的农业导航,减少了人类干预. 虽然较慢,但与传统的SLAM算法相比,它在精度和自主性方面表现出色.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 户外农业航行面临着诸如地形不平坦和通道相似性等挑战.
- 同时定位和映射 (SLAM) 算法和基于深度神经网络 (DNN) 的行为克隆 (BC) 是导航方法.
- 对于农业应用,评估这些方法至关重要.
研究的目的:
- 在户外农业环境中对SLAM算法和基于DNN的BC导航进行比较分析.
- 在精度,速度和自动化场景中评估性能.
- 建立选择最佳导航技术的标准.
主要方法:
- 将SLAM分为基于激光和基于视觉的方法.
- 实现并测试了基于DNN的BC导航.
- 在三个场景中使用加权,规范的性能指标 (P) 评估算法.
主要成果:
- 基于DNN的BC在精确性和自主性场景中实现了0.92的性能得分.
- 当速度被优先考虑时,RTAB-Map (一种SLAM算法) 获得了0.96分.
- Gmapping (一种SLAM算法) 显示了与DNN基础的BC在Autonomy中的性能 (0.92) 相当的性能.
结论:
- 基于DNN的BC导航是农业导航的可行,高效的替代方案,减少了人类干预.
- 导航技术的选择取决于优先级:基于DNN的BC用于精度/自主性,RTAB-Map用于速度,Gmapping用于自主性.
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