Cucumber Robotic Continuous Harvesting: Enhanced YOLOv8n Detection and Dynamic Bézier Curve-Assisted Collision-Free Path Generation

To address the inefficiency of the long single-fruit grasping cycle in traditional fruit harvesting robots, this study proposes a collision-free continuous harvesting solution for cucumber cultivation scenarios, coupled with a customized robotic system equipped with a continuous harvesting end-effector. In terms of visual perception, the YOLOv8n model is enhanced by integrating the GhostNet lightweight architecture, the Context-Guided Fusion Module (CGFM), and the MPDIoU loss function. Ablation experiments confirm the optimal model configuration, and the optimized model achieves a reduced model size of 5.3 MB and computational load of 6.6 GFLOPs while improving the mean average precision (mAP@50) by 2.5%, which facilitates low-cost deployment. For path planning, an Enhanced Bézier Continuous Picking (EBCP) algorithm is developed by combining 3D Gaussian kernel modeling and cubic Bézier curves to generate collision-free continuous trajectories. Simulation and practical experiments demonstrate that the path length of the proposed continuous picking method is only 31.1% that of the traditional path, with a theoretical collision-free rate of 96.69% and an actual collision-free rate of 92.24%. The feasibility and effectiveness of the proposed system are fully verified, providing a technical reference for the continuous operation of fruit harvesting robots.