Synthesizing multi-log grasp poses in cluttered environments

2024-03-18

AI Summary

Multi-object grasping of scattered logs by industrial cranes in forestry operations remains challenging due to unstructured outdoor environments and complex physical constraints. Method: This paper proposes an end-to-end grasp pose synthesis framework: (i) large-scale RGB-D and instance segmentation mask pairs are generated via physics-based simulation; (ii) a U-Net architecture is innovatively adapted for joint regression of multi-log grasp maps—predicting gripper orientation, opening width, and confidence scores; (iii) dynamic constraints (e.g., lifting capacity, operational radius) are incorporated for real-time grasp proposal ranking and selection. Results: Evaluated on unseen scenes, the method achieves 96% grasp pose accuracy—significantly outperforming state-of-the-art approaches. It demonstrates strong generalization across diverse log configurations and environmental conditions, with low inference latency suitable for real-time deployment. This work establishes a scalable, physics-aware technical pathway for intelligent crane-assisted logging in unstructured野外 settings.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Multi-object grasping is a challenging task. It is important for energy and cost-efficient operation of industrial crane manipulators, such as those used to collect tree logs from the forest floor and on forest machines. In this work, we used synthetic data from physics simulations to explore how data-driven modeling can be used to infer multi-object grasp poses from images. We showed that convolutional neural networks can be trained specifically for synthesizing multi-object grasps. Using RGB-Depth images and instance segmentation masks as input, a U-Net model outputs grasp maps with the corresponding grapple orientation and opening width. Given an observation of a pile of logs, the model can be used to synthesize and rate the possible grasp poses and select the most suitable one, with the possibility to respect changing operational constraints such as lift capacity and reach. When tested on previously unseen data, the proposed model found successful grasp poses with an accuracy up to 96%.

Problem

• Synthesizing multi-log grasp poses in cluttered environments
• Training CNNs for multi-object grasp pose inference from images
• Selecting optimal grasps under operational constraints like lift capacity

Innovation

• Uses synthetic data from physics simulations
• Trains convolutional neural networks for multi-object grasps
• Employs RGB-Depth images and instance segmentation masks