用多传感器动态识别苹果采摘成败，提前0.09秒预警失败
An analysis of sensor selection for fruit picking with suction-based grippers
- 按采摘不同阶段筛选最有效的传感器组合
- 在真实果园中准确率达90%以上，提前0.09秒预测失败
- 适合机器人采摘系统开发者优化感知方案
机器人采摘常无法可靠判断果实是否成功摘取，导致效率低且易损伤作物。此问题源于果实和夹爪的柔顺性、果梗连接方式多样以及果园环境遮挡。已有研究尝试基于视觉和多传感器学习进行采摘状态估计，但针对精准采摘与滑脱检测的最小传感器配置及阶段依赖感知策略仍缺乏探索。本文设计并评估了集成于柔性吸力式苹果夹爪的多模态传感套件。方法独特之处在于识别不同采摘阶段最具信息量的传感器，实现失败前的预测性检测。贡献包括对多模态传感器的阶段依赖性评估，以及可靠采摘状态分类所需的最小传感器集。在真实苹果果园中的实验表明，随机森林与多层感知机分类器在成功采摘和潜在失败检测上准确率均超过90%，且随机森林可于0.09秒内预测采摘/滑脱事件，接近人工标注的真实时间。
原文摘要 · Abstract (English)
Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However, minimal sensor sets and phase-dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction-based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase-dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human-annotated ground truth.
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