Forklift Pickup Control Using Point Clouds for Unconstrained Truck Parking
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Solution Overview
Problem
Unmanned forklifts face challenges in efficiently unloading cargoes due to the need for constrained truck parking locations and fixed positioning, leading to low pickup efficiency.
Innovation Solution
A method and system that utilizes point cloud data to determine the pose and pickup priority of carriers in a truck parking area, enabling automatic control of a forklift for efficient cargo handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If unmanned forklifts are used to unload cargoes with constrained truck parking locations and fixed positioning, then automation is achieved, but pickup efficiency is low
Solution Approach 1:
The system dynamically determines carrier poses and pickup priorities in real-time based on observed point cloud data, allowing the forklift to adapt to varying truck parking locations and cargo configurations without fixed positioning constraints, thereby improving pickup efficiency while maintaining automation
Solution Approach 2:
The system continuously observes the truck parking area, processes point cloud data to identify carrier poses, and adjusts pickup operations based on real-time feedback about carrier positions and priorities, enabling efficient automated operation without predefined constraints
2Ease of operation
If manual forklift handling is used, then flexibility is maintained, but labor cost increases and efficiency decreases
Solution Approach 1:
The forklift system performs self-navigation and self-operation by automatically determining carrier poses and pickup priorities from observed point cloud data, eliminating the need for manual intervention while maintaining operational flexibility and improving efficiency
3Device complexity
If truck parking locations are constrained and positioning is fixed, then unloading requirements are simplified, but pickup efficiency is reduced
Solution Approach 1:
The system performs preliminary observation and processing of point cloud data to determine carrier poses and pickup priorities before pickup operations begin, enabling the forklift to efficiently handle varied parking locations without requiring simplified constraints
Solution Approach 2:
The system changes the approach from fixed positioning to dynamic pose determination by processing point cloud data, allowing the forklift to adapt to different parking locations and maintain high pickup efficiency without simplifying unloading requirements
Data Source
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AI summary
A method and apparatus for forklift pickup, a computer device, and a storage medium are provided in the disclosure. The method includes the following. Observational data of a truck parking area is obtained by observing the truck parking area. Point cloud data of at least one truck is obtained in the observational data, and point cloud data of each of the at least one carrier is obtained in the point cloud data of the at least one truck. A relative pose of each of the at least one carrier and a pickup priority are determined based on the point cloud data of each of the at least one carrier. A forklift is controlled to perform pickup according to the relative pose of each of the at least one carrier and the pickup priority. The forklift can be automatically controlled to perform pickup, and pickup efficiency can be improved.