Container Crane Spreader Vision System for Non-Standard Chassis Alignment
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Solution Overview
Problem
Container cranes face challenges in accurately and quickly recognizing and aligning with non-standard vehicle chassis outside of terminals, leading to inefficiencies in container landing processes, especially when dealing with road chassis lacking recognizable structures.
Innovation Solution
A method using cameras on the spreader to make images of the landing target, identify features, calculate distances, and estimate orientation angles, employing similar triangles and statistical processing to determine precise positioning for container landing, which can handle various landing targets including terminal and road chassis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If laser scanners mounted on the trolley are used to detect chassis position, then automated container landing can be achieved, but the detection accuracy deteriorates when dealing with nonstandard road chassis lacking recognizable structures
Solution Approach 1:
The patent introduces cameras as an intermediary detection device between the crane system and the chassis. Instead of relying directly on laser scanners to detect chassis features, the cameras capture images that are processed to identify chassis position and orientation. This intermediary approach allows the system to work with diverse chassis types including road chassis without standardized structures.
Solution Approach 2:
The patent replaces the mechanical/optical laser scanning system with an image-based detection system using cameras. The cameras mounted on the spreader capture visual information about the chassis, which is then processed through image analysis algorithms to determine position and orientation, substituting direct mechanical/optical measurement with visual sensing and computational processing.
2Measurement precision
If standard terminal chassis with recognizable structures are used, then laser scanner detection is accurate and fast, but the system cannot handle diverse road chassis outside terminals
Solution Approach 1:
The patent makes the detection system universal by using cameras that can detect various types of chassis structures. The image processing system is designed to identify different chassis features (twistlocks, corner fittings, frame structures) across multiple chassis types including both standard terminal chassis and diverse road chassis, allowing one system to serve multiple functions and handle diverse landing targets.
Solution Approach 2:
The patent changes the detection parameters from relying on specific standardized chassis structures to using general visual features that can be detected across different chassis types. The system adjusts its detection approach based on what features are visible in the camera images, allowing it to adapt to different chassis geometries and structures without requiring standardized recognizable patterns.
3Adaptability or versatility
If manual operation is used for container landing, then flexibility in handling different chassis is maintained, but productivity and unloading speed decrease
Solution Approach 1:
The patent enables the system to serve itself by implementing automated detection and control. The cameras automatically capture chassis images, the processing system automatically identifies position and orientation, and the crane control system automatically adjusts the spreading mechanism. This self-service automation maintains flexibility in handling different chassis types while eliminating the need for manual intervention, thereby preserving both adaptability and productivity.
Solution Approach 2:
The patent implements a feedback loop where camera images of the chassis are continuously processed to determine position and orientation, and this information is fed back to the crane control system to automatically adjust the spreading mechanism. This closed-loop feedback system enables automated adaptation to different chassis types while maintaining high speed operation, combining the flexibility of manual operation with the productivity of automation.
4Manufacturing precision
If the spreader is manually adjusted to align with the chassis, then precise container landing is achieved, but the process time increases and productivity decreases
Solution Approach 1:
The patent performs preliminary detection and calculation of the required spreader alignment before the actual landing operation. The cameras capture chassis images and the processing system calculates the necessary spreader position and orientation in advance, allowing the crane control system to pre-position the spreader correctly before the container is lowered, eliminating time-consuming manual adjustments during the critical landing phase.
Solution Approach 2:
The patent replaces manual mechanical adjustment of the spreader with automated electronic control based on image processing. The system uses camera images to automatically calculate and control the spreader's position and orientation, substituting slow manual mechanical adjustments with fast electronic sensing and actuation, thereby achieving precise alignment without time loss.
Data Source
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AI summary
A method for automatically landing a container (2) on a landing target (19) using a container crane (1) is disclosed. The container crane comprises a trolley (3) and spreader (5) for holding and lifting the container and a crane control system (6) for controlling movements of said container crane. A distance (h) from the container to the landing target is measured and the container is moved towards the landing target dependent on the measured distance. A plurality of images of the landing target are made using at least one camera (13-13n) mounted on the spreader. The images are processed to identify one or more landing features (TL1-8) in the images of the landing target (19, 19', 16, 29, 39). Distances from the container to the landing target are calculated based on a measurement of distance between the container and the landing features in the images.