Pallet Load Stability Estimation Using AI Vision and Depth Sensing
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Solution Overview
Problem
In warehouse logistics, ensuring pallet load stability during automated material handling by autonomous mobile robots is challenging due to confined spaces and the need for precise determination of load orientation and size to prevent accidents and inefficiencies.
Innovation Solution
An AI-powered autonomous mobile robot equipped with sensors like cameras and LIDAR estimates pallet load stability by generating masks for the load and pallet using machine-learning models, determining orientation and size, and assessing whether it is safe to lift the pallet based on this data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to maintain pallet load stability, then stability can be maintained through even weight distribution, but the process is labor-intensive and prone to human error
Solution Approach 1:
The patent replaces manual visual inspection and judgment with an automated computer vision system using cameras and machine learning models. The system automatically detects load orientation, overhang, and stability characteristics, eliminating the need for manual assessment while improving reliability through consistent algorithmic evaluation.
Solution Approach 2:
The autonomous mobile robot performs self-assessment of load stability using its onboard sensors and AI models. The robot independently determines whether a pallet load is safe to transport without human intervention, making the system self-sufficient in stability evaluation.
2Productivity
If autonomous mobile robots are introduced to automate material handling, then productivity increases, but determining load stability in confined spaces becomes more challenging
Solution Approach 1:
The patent transitions from 2D camera images to 3D spatial understanding by integrating depth data from LIDAR sensors with 2D image data. This multi-dimensional approach enables accurate measurement of load orientation, overhang distance, and stability characteristics even in confined warehouse spaces where traditional measurement would be difficult.
Solution Approach 2:
The system introduces machine learning models as intermediaries between raw sensor data and stability determination. These AI models process complex sensor inputs and translate them into meaningful stability assessments, making the detection process more reliable and easier to implement in autonomous robots.
3Reliability
If precise determination of load orientation and size is performed to prevent accidents, then safety improves, but the complexity of the detection system increases
Solution Approach 1:
The patent divides the detection task into separate processing streams: one for detecting load orientation from camera images using machine learning models, and another for measuring overhang distance using LIDAR depth data. This segmentation allows each subsystem to specialize in specific measurements, improving accuracy while managing system complexity through modular architecture.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables safe and efficient pallet handling by accurately determining load stability, preventing accidents and improving handling efficiency in confined spaces.
Implementation Method 1
The one or more sensors may also include LIDAR configured to capture depth data comprising information indicating distance of surfaces of the load or the pallet from the one or more sensors
Data Source
AI summary
An autonomous mobile robot receives sensor data from one or more sensors. The sensor data includes image data depicting a load coupled to a pallet and depth data indicating distance of surfaces of the load or the pallet from the one or more sensors. A first machine-learning model is applied to the image data to generate a first mask and second mask. The first mask represents the load, and the second mask represents the pallet. The first mask, the second mask, and/or the depth data are then used to determine a load orientation and load size. Based on the load orientation and load size, the robot evaluates the load's stability. If the stability is deemed safe, the robot is caused to lift the pallet.