Robotic State Estimation Using Simulated Noise for Palletizing
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Solution Overview
Problem
Current systems face challenges in efficiently palletizing and depalletizing heterogeneous items due to the variety of sizes, weights, and types of items, which leads to instability and inefficiency, especially when using robotics, as they struggle to account for noise in sensor data and geometric models.
Innovation Solution
A robotic system that uses a combination of geometric models and programmatically generated noise data to simulate the placement of items, allowing for accurate state estimation and improved planning in palletization and depalletization processes, incorporating machine learning to model noise and adjust for inaccuracies in sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic systems are used for palletizing heterogeneous items, then productivity increases, but measurement precision deteriorates due to noise in sensor data and geometric models
Solution Approach 1:
The patent creates a simulated copy of the physical palletizing environment including virtual items, conveyor systems, and robotic manipulators. This digital twin allows the system to test and validate state estimation algorithms without requiring perfect real-world sensor data, thereby maintaining high productivity while improving measurement precision through simulation-based calibration and noise characterization.
Solution Approach 2:
The patent performs preliminary simulation and validation of state estimation algorithms in the virtual environment before deploying them to the physical robotic system. By pre-characterizing noise patterns and validating estimation accuracy in simulation, the system prepares robust algorithms that can handle real-world sensor noise, thus maintaining both high productivity and measurement precision.
2Manufacturing precision
If geometric models are used to simulate item placement, then manufacturing precision improves, but device complexity increases due to the need for sophisticated state estimation systems
Solution Approach 1:
The patent uses a simplified geometric model that copies only the essential features needed for placement accuracy (item dimensions, center of gravity, basic shape) rather than attempting to model all physical properties. This selective copying maintains manufacturing precision while reducing device complexity by focusing computational resources on the most critical geometric parameters.
Solution Approach 2:
The patent dynamically adjusts the level of geometric model complexity based on the specific placement context. For stable placements, simpler models suffice, reducing computational complexity. For critical placements requiring high precision, the system activates more detailed geometric parameters and state estimation algorithms, thus optimizing the balance between manufacturing precision and device complexity.
3Reliability
If simulated noise is added to geometric models, then reliability of state estimation improves, but loss of information increases due to noise in sensor data
Solution Approach 1:
The patent intentionally introduces simulated noise into the geometric models and state estimation algorithms during training and validation. By exposing the system to controlled noise patterns that mimic real sensor imperfections, the algorithms learn to filter and compensate for noise, converting the harmful effect of noise into a beneficial training mechanism that improves reliability while preserving essential information through learned denoising patterns.
Solution Approach 2:
The patent implements feedback loops where the simulated noisy data is processed through state estimation algorithms, and the results are compared against ground truth from the simulation. This feedback mechanism allows the system to continuously refine its noise filtering capabilities, improving reliability by learning to distinguish signal from noise while minimizing information loss through adaptive thresholding and validation.
Data Source
AI summary
A robotic system is disclosed. The system includes a memory configured to store estimated state information associated with a computer simulation of a robotic operation to stack a plurality of items on a pallet or other receptacle. The system includes one or more processors coupled to the communication interface and configured to perform the computer simulation. The computer simulation is performed at least in part by combining geometric model data based on idealized simulated robotic placement of each item with programmatically generated noise data. The programmatically generated noise data reflects an estimation of the effect that one or more sources of noise in a real-world physical workspace with which the computer simulation is associated would have on a real-world state of the plurality of items and/or the pallet or other receptacle if the plurality of items were stacked on the pallet or other receptacle as simulated in the computer simulation.