Robot Picking Simulation Using Sensor-Measurable Bulk Pile Regions
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Solution Overview
Problem
Current robot simulation systems fail to accurately simulate picking motions from bulk piles due to limitations in measuring workpiece regions with steep inclinations or specular reflections, leading to discrepancies between simulation and actual operations.
Innovation Solution
A robot simulation apparatus that estimates and simulates regions difficult to measure by a sensor unit, allowing for the simulation of picking motions from bulk piles in a virtual workspace, considering blind spots and measurement difficulties, thereby reducing the gap between simulation and actual operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete three-dimensional shape data of workpiece models is used in simulation, then the simulation can detect all regions of workpieces, but the simulation results do not match actual operation results because sensor units cannot measure regions with steep inclinations or specular reflections
Solution Approach 1:
The patent applies local quality by differentiating between measurable and non-measurable regions of workpieces. The system identifies specific regions (such as those with steep inclinations or specular reflections) that cannot be measured by sensor units and excludes only those specific regions from simulation data, while retaining complete data for other measurable regions. This selective exclusion approach maintains high simulation accuracy without unnecessarily sacrificing simulation completeness.
2Area of stationary object
If all regions of workpiece models including non-measurable regions are included in simulation, then the simulation achieves complete coverage, but grasping operations fail in actual execution because sensor units cannot acquire data from blind spots
Solution Approach 1:
The patent extracts and removes non-measurable regions from the simulation data. By identifying regions that cannot be measured by sensor units (such as blind spots, steep inclination areas, and specular reflection regions) and excluding only those specific regions from the simulation model, the system ensures that simulation data corresponds exactly to actually measurable workpiece regions, thereby improving grasping operation success rate.
3Device complexity
If the simulation uses ideal three-dimensional measurement data without considering sensor limitations, then the simulation model is simple and complete, but it does not reflect real-world measurement constraints leading to simulation-actual operation discrepancies
Solution Approach 1:
The patent applies preliminary action by pre-identifying and excluding non-measurable regions from workpiece models before simulation execution. The system determines in advance which regions cannot be measured by sensor units (such as regions with steep inclinations, specular reflections, or located in blind spots) and removes those regions from simulation data beforehand. This preliminary processing ensures that simulation data accurately reflects actual measurement capabilities without requiring complex real-time adjustments during simulation or operation.
Data Source
AI summary
The robot simulation apparatus includes: a workpiece model setting unit 11 that sets a workpiece model obtained by forming a model of a three-dimensional shape of a workpiece; a bulk pile data generating unit 20 that generates virtual bulk pile data of a plurality of workpiece models piled up in a virtual work space as a virtually formed work space; a region estimating unit 22 that identifies an estimated region that is estimated to be three-dimensionally measurable by a sensor unit which is disposed above the work space, based on a position and a posture of each workpiece model in the bulk pile data; and a picking motion simulating unit 30 that executes a simulation for verifying the picking motion from the bulk pile of the workpiece models in the virtual work space, based on data of the estimated region identified by the region estimating unit 22.