Jiawei Fan ,  Guohang Lu ,  Nianzu Dai , Xuemei Liu  ,  Ping Zhang , Jin Yuan

Abstract 

Frequent rainfall and strong winds necessitate timely and phased cotton harvesting, thereby driving advances in robotic harvesting. However, the irregular orientation and dense distribution of cotton bolls pose a major challenge to achieving efficient harvesting with a low missed-pick rate. To address this challenge, a multi-orientation sequential picking planning method is proposed, integrating an on-the-move picking strategy and a multi-surface picking end-effector capable of harvesting cotton bolls from multiple orientations. First, the OrientCot-YOLO model is proposed for the orientation identification and localization of cotton bolls with high inference speed to form a robust foundation for next multi-surface picking plan. Then, a directional hybrid weighting method is proposed to optimize on-the-move picking sequence planning, which integrates directional and three-dimensional distance weighting for target prioritization, and identifies the optimal picking surface through normal vector matching with cosine similarity. Finally, an optimal picking path planning method based on multi-surface picking end-effector is developed. The method integrates multi-objective optimization and speed dynamic compensation to calculate the end-effector pre-picking point, and employs RRT-Connect with NURBS smoothing to generate the optimal picking path. In the gazebo simulation experiment (506 cotton bolls), compared with the Random Traversal and Euclidean Distance methods, the MOSP method improved the picking efficiency by 34.9% and 15.8%, respectively, while reducing the omission rate by 22.34% and 13.85%. In field trials (200 bolls), the omission rate was 13.7% and the picking efficiency was 6.9 s/boll, including efficiency losses caused by failed picks due to branch movement. This method improves picking efficiency and reduces the omission rate of cotton picking, providing an effective solution for on-the-move picking in complex and dense environments.

Paper Linkage：https://www.sciencedirect.com/science/article/pii/S0168169926004916