基于ResNet-18的多任务视觉推理和自适应控制,用于边缘部署的自主机器人
Sufola Das Chagas Silva Araujo1, Goh Kah Ong Michael2, Uttam U Deshpande3
1Department of Computer Science and Engineering, Padre Conceição College of Engineering, Goa, India.
Frontiers in robotics and AI
|November 20, 2025
概括
本研究介绍了一种用于中小企业 (SMEs) 的自主物流机器人. 机器人集成导航,感知和处理在边缘计算平台上,提供一个具有成本效益的自动化解决方案.
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
- 边缘计算 边缘计算
背景情况:
- 工业机器人往往对中小企业来说过于复杂和昂贵.
- 现有的解决方案可能会严重依赖外部计算资源,限制自主性.
研究的目的:
- 为中小企业开发一个具有成本效益的自主物流机器人.
- 在边缘平台上集成自适应控制,视觉感知和机械处理.
主要方法:
- 使用一个NVIDIA Jetson Nano和一个修改后的ResNet-18模型进行并发任务执行.
- 实施了一种轻量级的支架式机械机制,用于起重有效载荷 (最多2公斤).
- 集成物体处理区域识别,障碍物检测和路径跟踪.
主要成果:
- 实现了92%的路径跟踪精度,88%的避难障碍成功和90%的物体处理成功.
- 展示了150毫秒的最大感知到行动的延迟时间.
- 在一次充电后可保持长达3小时的稳定运行.
结论:
- 开发的机器人为中小企业提供了一种实用,低功耗,独立的自动化解决方案.
- 这种综合系统解决了当前工业机器人的复杂性,成本和外部依赖性的局限性.
- 该机器人显示出在提高中小企业物流和运营效率方面的巨大潜力.
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