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Updated: Apr 6, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Occlusion Avoidance for Harvesting Robots: A Lightweight Active Perception Model.

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

Summary

This summary is machine-generated.

This study introduces an occlusion avoidance method for fruit harvesting robots using a lightweight YOLOv8n model and active perception. The system improves fruit detection and robotic arm efficiency in complex orchard environments.

Keywords:

active per ception strategy harvesting robot lightweight YOLOv8n occlusion avoidance

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Last Updated: Apr 6, 2026

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Published on: October 3, 2020

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Fruit harvesting robots struggle with fruit recognition and localization due to occlusion in complex orchards.
Existing methods lack real-time performance and effective occlusion handling.

Purpose of the Study:

To develop an occlusion avoidance method for fruit harvesting robots.
To improve fruit recognition and localization accuracy in cluttered environments.
To enhance the operational efficiency of agricultural robots.

Main Methods:

Developed a lightweight YOLOv8n model with C2f-FasterBlock and SE attention for real-time fruit detection.
Designed an end-to-end active perception model (ResNet50, multi-modal fusion) for occlusion avoidance.
Trained the model using a dataset from robot exploration in real-world scenarios.

Main Results:

The YOLOv8n model achieved 0.885 mAP, 83 FPS, and reduced model size (4.3 MB).
The active perception system guided the robotic arm to minimize occlusion, improving target recognition success rates.
The integrated system demonstrated enhanced operational efficiency in simulated complex environments.

Conclusions:

The proposed method effectively addresses occlusion challenges in fruit harvesting robots.
This approach offers a feasible pathway for developing robust agricultural robots for complex environments.
Further validation in diverse real-world conditions and system optimization are recommended.