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This paper investigates the optimal sensor configurations for detecting successful fruit picks using a suction-based robotic gripper. They perform a phase-dependent evaluation of multimodal sensors (force, torque, vacuum pressure, and proximity) during the picking process to identify the most informative sensor subsets at each stage. Results from apple orchard experiments demonstrate that Random Forest and Multilayer Perceptron classifiers, using minimal sensor sets, achieve over 90% accuracy in detecting successful picks and predicting failures.
Knowing *when* to listen to *which* sensor lets robotic fruit pickers predict failures before they happen, boosting accuracy to 90% even with minimal sensor sets.
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.
