Transport Structure Identification From Partial 2D Scans
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Solution Overview
Problem
Existing systems struggle to accurately identify and manipulate transport structures like pallets and containers in environments using autonomous vehicles due to variations in configuration and visibility issues caused by factors such as shrink-wrap or partial scanning.
Innovation Solution
An autonomous vehicle employs a 2D scan to identify features, calculate potential configurations, compare them to predefined models, and assign confidence scores to determine the most likely transport structure, using sensors like LIDAR and a control system to maneuver the vehicle accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scanning methods are used to identify transport structures, then the system can detect visible features, but it fails to accurately identify structures when partially obscured by shrink-wrap or other factors
Solution Approach 1:
The system performs multiple partial scans of the transport structure from different positions and angles rather than requiring a single complete scan. By accumulating data from multiple partial observations, the system can reconstruct the full configuration even when individual scans are partially obscured by shrink-wrap or other factors.
Solution Approach 2:
The system creates multiple candidate configuration models based on the scanned features and compares them against known transport structure templates. This copying approach allows the system to identify the correct structure by matching observed features against predefined models, even when the actual structure is partially hidden.
2Loss of information
If the system scans the entire space to identify all features, then it can capture complete information about transport structures, but this increases the time required for identification
Solution Approach 1:
The system performs preliminary scans to quickly identify the presence and general location of transport structures before conducting more detailed analysis. By pre-identifying regions of interest, the system can focus subsequent scanning efforts on specific areas, reducing the total time required while ensuring complete feature detection.
Solution Approach 2:
The scanning process is divided into multiple segments or phases: initial detection scan, detailed feature scan, and verification scan. Each segment focuses on specific aspects of the transport structure, allowing the system to gather complete information efficiently without requiring a single exhaustive scan that would take excessive time.
3Measurement precision
If the autonomous vehicle uses complex calculations to determine all possible configurations, then it can achieve high accuracy in identifying transport structures, but this increases computational complexity
Solution Approach 1:
The system extracts only the essential features and characteristics from the scan data that are necessary for identification, rather than processing all possible configuration parameters. By focusing on key discriminative features such as pocket locations, pillar positions, and overall dimensions, the system reduces computational complexity while maintaining high identification accuracy.
Solution Approach 2:
The system uses confidence scores as feedback to guide the identification process. When a candidate configuration achieves a sufficiently high confidence score, the system can terminate further calculations and accept that identification. This feedback mechanism prevents unnecessary complex calculations while ensuring accurate identification through iterative refinement of candidate configurations.
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
Effectively identifies and interacts with transport structures by determining their configuration with high accuracy, even in partially visible conditions, enabling precise handling and movement.
Implementation Method 1
using sensors like LIDAR
Data Source
AI summary
An example method is performed by one or more processing devices and includes the following: identifying one or more features based on data obtained from a two-dimensional scan of a space, where the data includes predefined characteristics; identifying physical attributes of the one or more features; performing calculations based on the physical attributes for the one or more features, where the calculations produce one or more possible configurations for one or more candidate transport structures in the space; comparing the one or more possible configurations to one or more predefined configurations for one or more known transport structures; identifying which, if any, of the one or more candidate transport structures is most likely to be a known transport structure based on the comparing; and controlling an autonomous vehicle based on the identifying.