农业机械学报2026，Vol.57Issue(5)：82-94,13.DOI:10.6041/j.issn.1000-1298.2026.05.007
基于ALH-SAC算法的柑橘采摘机械臂无碰撞抓取路径规划
Collision-free Grasp Path Planning for Citrus-picking Manipulator Based on ALH-SAC Algorithm
摘要
Abstract
In complex orchard environments,robotic fruit picking is challenged by branch occlusions,positioning inaccuracies,and low operational efficiency.A deep reinforcement learning-based framework for collision-free and efficient citrus harvesting with a robotic manipulator was proposed.The system integrated a YOLO 11 perception model for fruit classification and localization,together with a pressure prediction module for a flexible gripper to enable stable and damage-free grasping.An improved path planning algorithm,termed Attention-LSTM-HER-SAC(ALH-SAC),was developed to achieve adaptive and reliable grasp control.The proposed algorithm extended the soft actor-critic framework by incorporating hindsight experience replay,an LSTM encoder for modeling temporal joint dependencies,and an attention mechanism to emphasize critical spatial features under occlusion.A customized reward function was designed to balance rapid target approach,obstacle avoidance,and optimal grasp orientation.In addition,a hybrid guidance strategy combining heuristic priors with a Cartesian-space linear interpolation prior was introduced to accelerate training convergence and enhance robustness.Simulation results demonstrated that ALH-SAC achieved a positioning accuracy of(10.35±4.18)mm,outperforming HER-TD3 and HER-DDPG baselines by over 12.5 mm on average.Orchard experiments further confirmed the effectiveness of the proposed method,achieving an average picking success rate of 84% and an average operation time of 10.58 s,validating its practical applicability in real-world citrus harvesting.关键词
柑橘采摘/机械臂/路径规划/SAC算法/柑橘检测/YOLO 11Key words
citrus picking/robotic arm/path planning/SAC algorithm/citrus detection/YOLO 11分类
农业科技引用本文复制引用
兰玉彬,宋皓昱,王莹,刘星宇,李想,黎源鸿..基于ALH-SAC算法的柑橘采摘机械臂无碰撞抓取路径规划[J].农业机械学报,2026,57(5):82-94,13.基金项目
国家自然科学基金项目(32301708)、国家重点研发计划项目(2023YFD2000200)、高等学校学科创新引智基地项目(D18019)和广东省科技创新战略专项资金项目(pdjh2025bc042) （32301708）
