Strawberry harvesting robots faced persistent challenges such as low integration of visual perception, fruit-gripper misalignment, empty grasping, and strawberry slippage from the gripper due to insufficient gripping force, all of which compromised harvesting stability and efficiency in orchard environments. To overcome these issues, this paper proposed a visual fault diagnosis and self-recovery framework that integrated multi-task perception with corrective control strategies. At the core of this framework was SRR-Net, an end-to-end multi-task perception model that simultaneously performed strawberry detection, segmentation, and ripeness estimation, thereby unifying visual perception with fault diagnosis. Based on this integrated perception, a relative error compensation method based on the simultaneous target-gripper detection was designed to address positional misalignment, correcting deviations when error exceeded the tolerance threshold. To mitigate empty grasping and fruit-slippage faults, an early abort strategy was implemented. A micro-optical camera embedded in the end-effector provided real-time visual feedback, enabling grasp detection during the deflating stage and strawberry slip prediction during snap-off through MobileNet V3-Small classifier and a time-series LSTM classifier. Experiments demonstrated that SRR-Net maintained high perception accuracy. For detection, it achieved a precision of 0.895 and recall of 0.813 on strawberries, and 0.972/0.958 on hands. In segmentation, it yielded a precision of 0.887 and recall of 0.747 for strawberries, and 0.974/0.947 for hands. For ripeness estimation, SRR-Net attained a mean absolute error of 0.035, while simultaneously supporting multi-task perception and sustaining a competitive inference speed of 163.35 FPS.

翻译：草莓采摘机器人长期面临视觉感知集成度低、果实与夹爪错位、空抓以及因夹持力不足导致的草莓从夹爪滑落等问题，这些均影响了果园环境下的采摘稳定性与效率。为克服这些难题，本文提出了一种融合多任务感知与校正控制策略的视觉故障诊断与自恢复框架。该框架的核心是SRR-Net，一个端到端的多任务感知模型，可同时执行草莓检测、分割与成熟度估计，从而将视觉感知与故障诊断统一起来。基于此集成感知，设计了一种基于目标-夹爪同步检测的相对误差补偿方法，以应对位置错位问题，在误差超过容差阈值时进行偏差校正。为缓解空抓与果实滑落故障，实施了早期中止策略。嵌入末端执行器的微型光学摄像头提供实时视觉反馈，使得在夹爪收缩阶段进行抓取检测，并通过MobileNet V3-Small分类器与时间序列LSTM分类器在采摘瞬间预测草莓滑落。实验表明，SRR-Net保持了较高的感知精度。在检测任务中，对草莓的精确率为0.895、召回率为0.813，对手部的精确率/召回率为0.972/0.958。在分割任务中，对草莓的精确率为0.887、召回率为0.747，对手部为0.974/0.947。在成熟度估计任务中，SRR-Net的平均绝对误差为0.035，同时支持多任务感知并保持了163.35 FPS的具有竞争力的推理速度。