Res-FLNet:基于学习控制算法的多模式感应机器人自动驾驶任务的人机交互和协作
1School of Automotive Engineering, Changzhou Institute of Technology, Changzhou, Jiangsu, China.
Frontiers in neurorobotics
|October 18, 2023
概括
Res-FLNet通过ResNet-50和LSTM增强了自动驾驶,使用联邦学习 (FL) 保护隐私. 这种方法可以确保机器人安全地分享知识,改善导航和人机协作.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动驾驶汽车和机器人需要先进的导航和人机交互 (HRI) 能力.
- 机器人 (摄像头,激光雷达,雷达) 的多式传感引发了严重的隐私和数据安全问题.
- 现有的方法很难在自主系统中平衡性能与数据隐私的需求.
研究的目的:
- 引入Res-FLNet,这是一个用于使用多式联络传感的保护隐私的自动驾驶的新框架.
- 利用联邦学习 (FL) 进行安全,分散的模型训练,对个人机器人进行训练.
- 通过保护隐私的知识共享来增强人机交互和协作.
主要方法:
- Res-FLNet集成ResNet-50用于视觉特征提取和长短期内存 (LSTM) 进行顺序数据分析.
- 联邦学习 (FL) 用于在机器人上本地训练模型,防止原始数据共享.
- 模型更新被集中聚合以建立集体智能,同时保持个人数据隐私.
主要成果:
- 在KITTI,Waymo,ApolloScape和BDD100K数据集上,Res-FLNet展示了卓越的准确性,稳定性和隐私保护.
- 该框架在自动驾驶任务中实现了最先进的性能.
- 实验证实了Res-FLNet在各种驾驶场景中的适应性和泛化能力.
结论:
- Res-FLNet为保护隐私的多式联运自动驾驶提供了有效的解决方案.
- FL的整合使得安全的知识共享成为可能,这对于推进HRI和协作至关重要.
- Res-FLNet显示出在复杂,动态环境中部署多模式传感机器人的巨大潜力.
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