Automated Guided Forklift Docking Using Radar-Camera Height Alignment
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Solution Overview
Problem
Automated guided forklifts face challenges in accurately docking with carriers due to non-standard sizes and irregular shapes of material cages, as well as uneven ground surfaces, leading to potential collisions and operational inefficiencies.
Innovation Solution
A system utilizing a controller with a processor, radar, and camera to fuse point cloud and image data for precise distance determination, enabling accurate insertion of the fork into docking holes by adjusting for ground clearance and carrier deformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated guided forklift uses standard docking procedures, then operation is simple and fast, but docking accuracy deteriorates when carriers have non-standard sizes or irregular shapes
Solution Approach 1:
The system performs preliminary scanning and recognition of the carrier's position, shape, and size before docking. The radar and camera capture data in advance to determine the carrier's characteristics, allowing the control algorithm to pre-calculate the optimal docking path and fork insertion depth, thereby achieving accurate docking with non-standard carriers without increasing operational complexity during the actual docking process
Solution Approach 2:
The system uses radar and camera sensors to continuously monitor the relative position and distance between the forklift and carrier during approach. The control algorithm processes this feedback data in real-time to dynamically adjust the forklift's trajectory and positioning, ensuring accurate docking even when carriers have irregular shapes or are placed on uneven surfaces
2Measurement precision
If the automated guided forklift adapts to non-standard carriers through complex recognition algorithms, then docking accuracy improves, but computation time and processing load increase
Solution Approach 1:
The recognition process is divided into distinct stages: initial carrier detection using radar, detailed shape and position analysis using camera imaging, and final docking parameter calculation. This segmentation allows each stage to use appropriately optimized algorithms, preventing unnecessary computational overhead while maintaining high detection accuracy throughout the docking process
Solution Approach 2:
The control system employs a universal docking algorithm that can handle both standard and non-standard carriers through a unified mathematical model. This multi-functional approach eliminates the need for separate recognition procedures for different carrier types, reducing computational complexity and processing time while maintaining accuracy across all carrier configurations
3Reliability
If the automated guided forklift uses a simple control algorithm, then processing speed is fast, but docking accuracy deteriorates on uneven ground or with carrier deformations
Solution Approach 1:
The system introduces an intermediary coordinate transformation layer that maps the complex, variable carrier positions and shapes onto a standardized reference frame. This intermediary representation simplifies the control calculations while preserving all necessary geometric information, allowing the forklift to accurately compensate for uneven ground and carrier deformations without requiring overly complex control algorithms
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 precise docking by minimizing collisions and improving operational efficiency, even in non-standard environments, through the integration of radar and camera data fusion for enhanced control algorithms.
Implementation Method 1
radar, and camera to fuse point cloud and image data
Implementation Method 2
controller with a processor, radar, and camera to fuse point cloud and image data
Data Source
AI summary
The present disclosure relates to an automated guided forklift, a method for controlling an automated guided forklift, and a controller. An automated guided forklift is described. The automated guided forklift includes a controller. The controller executes program instructions to perform operations including the following: receiving first optical data; determining depth information of an object based on the first optical data; receiving second optical data, the second optical data including optical information of the object and optical information of a fork of the automated guided forklift; determining a first height difference between the object and the fork based at least in part on the depth information and the second optical data; and moving the fork based at least in part on the first height difference until the first height difference satisfies an insertion condition.