A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting

2025-06-16

AI Summary

To address fruit occlusion-induced grasping failures in unstructured orchard environments, this paper proposes an end-to-end point cloud processing framework for strawberry harvesting. Methodologically, it pioneers the integration of generative point cloud completion into a closed-loop grasping pipeline, incorporating denoising segmentation, occlusion-aware 3D reconstruction, ripeness-aware target filtering, voxelized occupancy grid modeling, and collision-aware pose optimization with motion planning. Its key innovations include high-fidelity 3D shape reconstruction under occlusion (Chamfer distance: 1.10 mm, state-of-the-art) and joint modeling of fruit ripeness and environmental obstacles. Experiments in real-world orchard settings demonstrate a grasping success rate of 79.17%, a task completion rate of 89.58%, and a substantial reduction in obstacle collision rate—from 43.33% to 13.95%—achieving a balanced trade-off between operational success and safety.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In robotic fruit picking applications, managing object occlusion in unstructured settings poses a substantial challenge for designing grasping algorithms. Using strawberry harvesting as a case study, we present an end-to-end framework for effective object detection, segmentation, and grasp planning to tackle this issue caused by partially occluded objects. Our strategy begins with point cloud denoising and segmentation to accurately locate fruits. To compensate for incomplete scans due to occlusion, we apply a point cloud completion model to create a dense 3D reconstruction of the strawberries. The target selection focuses on ripe strawberries while categorizing others as obstacles, followed by converting the refined point cloud into an occupancy map for collision-aware motion planning. Our experimental results demonstrate high shape reconstruction accuracy, with the lowest Chamfer Distance compared to state-of-the-art methods with 1.10 mm, and significantly improved grasp success rates of 79.17%, yielding an overall success-to-attempt ratio of 89.58% in real-world strawberry harvesting. Additionally, our method reduces the obstacle hit rate from 43.33% to 13.95%, highlighting its effectiveness in improving both grasp quality and safety compared to prior approaches. This pipeline substantially improves autonomous strawberry harvesting, advancing more efficient and reliable robotic fruit picking systems.

Problem

• Handling object occlusion in robotic fruit picking.
• Completing partial point clouds for accurate 3D reconstruction.
• Improving grasp success rates and reducing obstacle collisions.

Innovation

• Point cloud denoising and segmentation for fruit location
• Point cloud completion for dense 3D reconstruction
• Occupancy map conversion for collision-aware planning