基于分层策略的双孢菇采摘任务规划方法

Hierarchical Strategy-based Task Planning Method for Agaricus bisporus Picking

摘要:

针对双孢菇密集堆叠的生长特性给机器人采摘带来的损伤难题,提出了一种基于分层策略的双孢菇采摘任务规划方法。该方法基于全局局部分层思想,采用YOLO 11模型根据遮挡程度对双孢菇进行分类检测并将采摘问题转换为旅行商问题建模。在全局层面,构建优先采摘遮挡蘑菇并通过检测更新环境信息的规划策略,以降低遮挡导致的碰撞损伤;在局部层面,对不同类别蘑菇分别设计改进模拟退火算法优化采摘路径以提升效率,开发自适应同心圆外围式采摘算法以降低密集分布导致的粘连损伤。仿真试验表明,改进模拟退火算法在20、40、60节点场景下路径长度分别优化19.2%、24.7%和35.0%,收敛效率分别提升61.5%、39.6%和18.2%。实机试验采用20组550个双孢菇样品验证,对比传统按高度采摘策略,该方法成功率达89.8%,碰撞率从5.09%降至2.16%,降幅达57.6%,验证了该方法在保证采摘效率的前提下能够有效降低采摘损伤。

Abstract:

Aiming to address the damage issues in robotic Agaricus bisporus harvesting caused by dense clustering growth patterns, a hierarchical strategy-based picking task planning method was proposed. Based on a global-local hierarchical framework, this method employed a YOLO 11 deep learning model to classify and detect mushrooms according to their occlusion degree, and formulated the picking sequence optimization problem as a traveling salesman problem. At the global planning level, a strategic framework was constructed to prioritize the harvesting of occluded mushrooms and dynamically updated environmental information through iterative detection processes, thereby effectively reducing collision damage caused by occlusion. At the local execution level, an improved simulated annealing algorithm was designed to optimize picking paths for different mushroom categories separately to enhance operational efficiency, and an adaptive concentric-layer peripheral picking algorithm was developed to minimize adhesion damage caused by dense spatial distribution. Simulation experiments demonstrated that the improved simulated annealing algorithm achieved path length optimizations of 19. 2% , 24. 7% , and 35. 0% in 20-, 40-, and 60-node scenarios respectively, with corresponding convergence efficiency improvements of 61. 5% , 39. 6% , and 18. 2% . Field experiments conducted with 20 groups comprising 550 mushroom samples validated that, compared with conventional height-based picking strategies, the proposed method achieved a success rate of 89. 8% , substantially reduced the collision rate from 5. 09% to 2. 16% ( a 57. 6% reduction), thereby validating that the method effectively reduced harvesting damage while maintaining operational efficiency.