Reliable selective picking of long-staple cotton remains challenging because dense dwarf canopies restrict robot operating space and increase boll occlusion, resulting in reduced target visibility and potential fiber damage during picking. To address these challenges, a mobile dual-arm robotic picking system integrating hierarchical depth perception, cotton-boll recognition, optimized motion planning, and three-finger flexible end-effectors was developed for autonomous picking in Xinjiang long-staple cotton fields. The proposed YOLOv7-DCN-SENet model reached 95.75% precision, 92.65% recall, and 97.19% mAP@0.5 on the test set, while the onboard computing platform operated at 101 FPS under the experimental configuration. Indoor and field experiments were conducted on directly visible upper-canopy open cotton bolls. The dual-arm robot achieved parallel picking success rates of 74.6% and 57.6%, with average picking cycles of 28.2 s and 34.9 s, respectively. Field performance was mainly limited by strong-light overexposure, depth-information loss, occlusion-induced localization errors, arm interference within narrow canopy spaces, and incomplete fiber separation during boll detachment. These results demonstrate the feasibility of autonomous dual-arm selective picking for long-staple cotton under dense planting conditions and provide a basis for further improvements in robotic cotton-picking systems.
Chen et al. (Sun,) studied this question.