Multi-arm robotic system and strategy for the automatic packaging of apples

Abstract

Packaging is a crucial step in the commercial distribution of apples after harvest. However, achieving fast and accurate automated packaging remains a challenge. This study proposed for the first time a highly integrated four-arm robotic system and an effective implementation strategy specifically designed for automatic packaging of apples. In order to achieve accurate and simultaneous detection of multi-channel apples in complex visual conditions, an enhanced target detection model (i.e. YOLOv8s-GUC) based on the YOLOv8s architecture was developed by combining with a global attention mechanism (GAM), UniRepLKNet large kernel convolution structure, and CARAFE lightweight upsampling module. The results demonstrated that the YOLOv8s-GUC model can significantly improve detection accuracy and generalization for apples and related small features (such as stems and calyxes), achieving an [email protected] of 99.5 %. Furthermore, considering the complexity and real-time constraints of task planning in multi-arm robotic operations, this study further proposed an intelligent scheduling algorithm based on a Deep Q-Network (DQN), which enables efficient collaboration and collision-free operation among the robotic arms through real-time state perception and online decision-making. The results of simulations and real-world experiments indicated that the developed multi-arm robotic packaging system and scheduling strategy had high operational stability and efficiency in apple packaging, with a success rate of 100 % and an average packaging time of less than one second per apple. This study provides an effective and reliable solution for automated apple packaging.