AGV Self-Location Estimation Using Storage Status Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous guided vehicles (AGVs) in container terminals face challenges in accurately estimating their self-location due to frequent changes in container storage situations, which can lead to incorrect map updates and navigation issues when using Simultaneous Localization and Mapping (SLAM) technology.
Innovation Solution
A self-location estimation device equipped with a map information acquisition unit, environmental information acquisition unit, and self-location estimation unit, connected to a storage facility management system, which generates and updates map information based on shape and storage status information of objects, allowing the AGV to accurately estimate its location and navigate within the facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SLAM technology is used for self-location estimation and map generation, then the AGV can perform self-location estimation and generation of map information while traveling, but the AGV cannot correctly determine whether to update the map information or correct the self-location estimation due to frequent container storage changes
Solution Approach 1:
The patent divides the self-location estimation process into two independent modules: one for map information acquisition (using LiDAR to scan and build maps of the container terminal environment) and another for self-location estimation (comparing current environmental information with stored map information). This segmentation allows the system to distinguish between actual location changes and environmental changes, resolving the contradiction between adaptability and measurement precision.
Solution Approach 2:
The patent introduces an intermediary comparison mechanism that takes both the acquired map information and current environmental information as inputs, compares them to determine whether changes are due to AGV movement or environmental changes, and then outputs the corrected self-location. This intermediary process prevents incorrect map updates while maintaining the ability to adapt to real location changes.
2Reliability
If the AGV updates map information based on detected changes, then the map information can be kept current, but the AGV may incorrectly update the map when container storage situations change, leading to inaccurate self-location estimation
Solution Approach 1:
The patent implements a feedback mechanism where the self-location estimation unit continuously compares current environmental information with stored map information, and only updates the map when the comparison confirms that the AGV has moved to a new location rather than the environment changing. This feedback loop prevents incorrect map updates while maintaining map currentness.
Solution Approach 2:
Instead of updating the map whenever changes are detected (conventional approach), the patent inverts the logic: it assumes the map is correct and only updates it when the comparison between current and stored information confirms actual AGV movement. This inversion prevents incorrect updates caused by environmental changes like container repositioning.
3Measurement precision
If the AGV uses transponder system with radio wave transmitters and responders, then self-location estimation can be performed, but time and effort are required to install radio wave responders in prescribed positions in advance
Solution Approach 1:
The patent enables the AGV to perform self-service mapping by using its own LiDAR sensor to scan and acquire map information of the container terminal environment during normal operations. The AGV builds and updates its own map without requiring external infrastructure installation, eliminating the need for pre-installed responders while maintaining accurate self-location estimation.
Solution Approach 2:
The patent replaces the mechanical infrastructure-based transponder system with an optical sensing system (LiDAR). Instead of using radio wave transmitters and responders embedded in the facility, the system uses light detection and ranging to acquire environmental information and generate maps, significantly reducing installation complexity while maintaining measurement precision.
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
Enables easy and accurate self-location estimation in facilities with objects of prescribed shapes, ensuring proper navigation and operation of AGVs even in dynamically changing environments.
Implementation Method 1
environmental information of surroundings acquired by Light Detection and Ranging (LiDAR)
Data Source
AI summary
A self-location estimation device includes a map information acquisition unit, an environmental information acquisition unit, and a self-location estimation unit. The map information acquisition unit is configured to acquire map information in a storage facility generated based on shape information of an object and storage status information of the object in the storage facility. The environmental information acquisition unit is configured to acquire environmental information of surroundings. The self-location estimation unit is configured to estimate a self-location based on the map information acquired by the map information acquisition unit and the environmental information acquired by the environmental information acquisition unit.