Autonomous Vehicle Marking Recognition for Precise Target Conveyance
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Solution Overview
Problem
Autonomous vehicles struggle to accurately recognize and convey conveyance targets, leading to potential collisions and inefficient operation.
Innovation Solution
The autonomous vehicle is equipped with sensors to capture images and markings, and a controller to identify and control conveyance targets based on these markings, enabling precise navigation and docking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicle uses conventional navigation methods without marking recognition, then device complexity is reduced, but measurement precision of conveyance target position deteriorates
Solution Approach 1:
The patent uses markings (barcodes, QR codes, or custom patterns) as visual copies or representations of conveyance target positions. These markings serve as simplified informational copies that the autonomous vehicle can read from a distance, replacing complex direct position measurement systems while maintaining high recognition accuracy.
Solution Approach 2:
The markings act as an intermediary between the autonomous vehicle's navigation system and the physical conveyance target positions. Instead of directly measuring position through complex sensors, the system uses these visual markers as intermediate reference points that bridge the gap between navigation commands and precise location identification.
2Reliability
If autonomous vehicle accurately recognizes conveyance target outer dimensions, then collision avoidance capability is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary recognition of markings that encode conveyance target information (including outer dimensions) before the vehicle approaches or contacts the target. This advance identification allows the autonomous vehicle to pre-calculate safe approach distances and navigation paths, ensuring collision avoidance is established before critical measurement phases occur.
Solution Approach 2:
The patent transitions from direct physical measurement of conveyance targets to indirect identification through visual markings. By encoding target information (including dimensional data) in two-dimensional visual patterns, the system obtains accurate dimensional information without requiring complex three-dimensional physical measurement, thus maintaining reliability while managing measurement precision requirements.
3Productivity
If autonomous vehicle uses marking-based identification system, then productivity of conveyance operation is improved, but device complexity increases
Solution Approach 1:
The marking system serves multiple functions simultaneously: it identifies conveyance targets, provides positional information, encodes outer dimensions, and enables collision avoidance calculations. This multi-functionality consolidates what would otherwise require separate systems into a single unified marking recognition approach, improving productivity while managing device complexity through functional integration.
Data Source
AI summary
An autonomous vehicle includes one or more sensors configured to obtain one or more images of a conveyance target that includes one or more markings; and a controller configured to control the autonomous vehicle. The controller includes one or more processors, and one or more memories storing one or more programs, which when executed, cause the one or more processors to identify the conveyance target based on a recognition result of the one or more markings included in the one or more images, and control conveyance of the conveyance target by the autonomous vehicle based on an identification result of the conveyance target.