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收获机器人的遮蔽回避:一个轻量级的积极感知模型

Tao Zhang¹, Jiaxi Huang¹, Jinxing Niu¹

¹School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China.

Sensors (Basel, Switzerland)

|January 10, 2026

概括

本研究介绍了一种用于采摘水果的机器人,使用轻量级YOLOv8n模型和主动感知来避免阻塞的方法. 该系统在复杂的果园环境中提高了水果检测和机器人臂效率.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

由于复杂的果园的封闭,果机器人难以识别和定位果实.
现有的方法缺乏实时性能和有效的阻塞处理.

研究的目的:

为水果采摘机器人开发一种避免堵塞的方法.
在混乱的环境中提高水果识别和定位准确度.
提高农业机器人的运行效率.

主要方法:

开发了一种轻量级的YOLOv8n模型,配备C2f-FasterBlock和SE注意力,用于实时检测水果.
设计了一个端到端的主动感知模型 (ResNet50,多模式融合) 以避免阻塞.
在现实场景中使用机器人探索数据集训练模型.

主要成果:

YOLOv8n模型实现了0.885 mAP,83 FPS,并减少了模型大小 (4.3 MB).
活动感知系统引导机器人手臂尽量减少遮,提高目标识别成功率.
综合系统在模拟复杂环境中证明了更高的运营效率.

结论:

拟议的方法有效地解决了水果收获机器人中阻塞的挑战.
这种方法为开发用于复杂环境的强大的农业机器人提供了可行的途径.
建议在各种现实条件下进行进一步验证和系统优化.

关键词:

主动感知策略是一个积极感知策略.收获机器人收获机器人轻量级的YOLOv8nn可以使用.避免堵塞避免堵塞

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Schemas

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