Automated Vehicle Sensor Array for Object Load Orientation Detection
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Solution Overview
Problem
Automated vehicles in warehouses face challenges in accurately determining object load orientation, especially when loads are stacked or wrapped, leading to inefficiencies and errors in handling and transportation due to limitations in existing sensing technologies.
Innovation Solution
A method and apparatus using a sensor array attached to an automated vehicle to process data from laser scanners and cameras, executing an object recognition process to identify rack systems and align lifting elements for precise engagement and disengagement of object loads by comparing sensor data with models, facilitating accurate orientation and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensing technologies are used to determine object load orientation, then the system is simple and cost-effective, but the measurement precision deteriorates when loads are stacked or wrapped
Solution Approach 1:
The sensing system is segmented into multiple specialized sensors (laser scanner for geometric shape detection, camera for visual identification, RFID reader for identification) rather than using a single conventional sensor. Each sensor type targets specific aspects of the object load to collectively achieve precise orientation determination even for stacked or wrapped loads.
Solution Approach 2:
Multiple sensing technologies (laser scanning, optical imaging, RFID) are merged into an integrated sensing system that processes data from all sources together. This combination allows the system to overcome the limitations of individual sensors and accurately determine object load orientation in complex scenarios where conventional single-sensor systems fail.
2Productivity
If human operators manually ascertain object load orientation, then the system requires no complex sensing technology, but productivity deteriorates due to human errors and time consumption
Solution Approach 1:
The system performs self-service by automatically detecting object load orientation, computing entry points, and positioning lifting elements without human intervention. The sensing system, processing unit, and control mechanism work autonomously to complete the entire object handling task, eliminating human errors and significantly improving productivity.
Solution Approach 2:
The manual mechanical process of human operators visually assessing and physically positioning loads is replaced with an automated system using laser scanners, cameras, and computer-controlled positioning. This substitution eliminates human limitations while achieving faster and more accurate object handling.
3Adaptability or versatility
If automated vehicles use pre-programmed paths, then the system is simple to implement, but adaptability deteriorates when floor conditions vary or objects are at raised positions
Solution Approach 1:
The navigation system transitions from static pre-programmed paths to dynamic real-time path planning. The automated vehicle uses laser scanner and camera data to continuously adapt its path based on current warehouse conditions, object positions, and floor variations, enabling operation in previously inaccessible areas such as high racking locations.
Solution Approach 2:
The system changes operational parameters by using real-time sensor data to adjust vehicle position, lifting element orientation, and path selection based on detected object characteristics. This allows adaptation to varied warehouse conditions including uneven floors, stacked loads, and raised positions that would be impossible with fixed pre-programmed paths.
Data Source
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AI summary
A method and apparatus for sensing object load engagement, transportation and disengagement by automated vehicles is described. In one embodiment, the method includes processing data that is transmitted from a sensor array comprising at least one device for analyzing a plurality of objects that are placed throughout a physical environment, executing an object recognition process on the sensor array data using model information to identify at least one object, determining orientation information associated with the at least one object, wherein the orientation information is relative to the lift carriage and positioning at least one lifting element based on the orientation information.