AGV Trajectory Control With Load-Based Parameter Switching
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Solution Overview
Problem
Existing AGV movement control methods, such as PID and fuzzy PID control, are ineffective under varying load conditions and require extensive user expertise and trial-and-error adjustments, leading to inefficiencies and increased commissioning time.
Innovation Solution
The method involves determining load identifiers along a predetermined trajectory, using RFID or QR codes, to adjust control parameters dynamically, allowing the AGV to adapt to different load conditions, thereby improving tracking accuracy and simplifying control algorithms for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional PID or fuzzy PID control methods are used for AGV movement control, then the control algorithm can be implemented, but the system requires extensive user expertise and trial-and-error adjustments, increasing commissioning time and complexity
Solution Approach 1:
The system pre-divides the trajectory into multiple segments with different load conditions before AGV operation. Each segment is pre-configured with appropriate control parameters, eliminating the need for real-time parameter tuning and reducing commissioning time.
Solution Approach 2:
The system automatically selects and switches control parameters based on the detected load condition segments. Instead of requiring manual parameter adjustment, the control parameters change automatically according to the pre-defined segment database, significantly reducing operational complexity.
2Manufacturing precision
If fixed control parameters are used throughout the entire trajectory, then the control system is simple to implement, but the AGV cannot adapt to varying load conditions, reducing tracking accuracy
Solution Approach 1:
The trajectory is divided into multiple segments, each associated with specific load conditions. This segmentation allows the system to apply different control parameters to different segments, improving tracking accuracy without requiring a completely complex adaptive control system.
Solution Approach 2:
The system introduces a segment database as an intermediary between the simple fixed-parameter control and the need for adaptive control. The database stores pre-configured control parameters for different load conditions, allowing automatic parameter selection without complex real-time calculations.
3Adaptability or versatility
If manual adjustment of control parameters is required for different load conditions, then the control can be customized, but the operation becomes complex and time-consuming
Solution Approach 1:
The system performs self-service by automatically detecting the current load condition segment and selecting the appropriate control parameters from the pre-configured database. This eliminates the need for manual parameter adjustment while maintaining adaptability to different load conditions.
Solution Approach 2:
The system uses feedback from load identification (through markers or sensors) to automatically adjust control parameters. The detected load condition feeds back to the control system, which then selects the appropriate parameters, creating a closed-loop system that is both adaptable and easy to operate.
4Manufacturing precision
If dynamic parameter adjustment is implemented to handle varying load conditions, then tracking performance improves, but the control algorithm complexity increases
Solution Approach 1:
The control parameters are pre-calculated and stored in a database for each trajectory segment before operation. This preliminary action eliminates the need for complex real-time parameter calculation algorithms, reducing computational complexity while maintaining tracking accuracy.
Solution Approach 2:
Instead of implementing complex adaptive control algorithms, the system uses a database copy of pre-optimized control parameters for different load conditions. The control system simply retrieves the appropriate parameter set from the database, avoiding complex calculations while achieving good tracking performance.
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
This approach enhances the AGV's ability to maintain accurate trajectory tracking under diverse load conditions, reduces commissioning time, and simplifies control parameter adjustments, providing a more convenient and effective movement control solution.
Implementation Method 1
the load identifier marker comprises one of: a radio frequency identification (RFID) tagger, or a Quick Response (QR) code; and the detector comprises one of: a RFID tagger reader, or a QR code reader
Implementation Method 2
the load identifier marker comprises one of: a radio frequency identification (RFID) tagger, or a Quick Response (QR) code
Data Source
AI summary
Embodiments of the present disclosure provide methods, devices, systems, and a computer readable medium for controlling an automatic guided vehicle (AGV). In some embodiments of the method, a first load identifier is determined at a first location on a predetermined trajectory of the AGV. The first load identifier represents a first load condition at the first location. A first set of values of a plurality of control parameters is determined based on the first load identifier. A movement of the AGV is controlled based on the first set of values. With these embodiments, the movement of the AGV can be controlled according to a set of values of control parameters based on the load condition.