IIoT AGV Route Planning for Material Transport Obstacles
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Solution Overview
Problem
On ultra-large-scale production lines, obstacles on AGV routes can significantly impact material transportation efficiency, requiring time-consuming re-planning of routes and increasing production line inefficiencies.
Innovation Solution
An Industrial Internet of Things system is implemented, comprising a user platform, service platform, and management platform connected through a sensor network, which obtains AGV route layouts, generates directed graphs, determines optimal paths, and controls AGVs to navigate around obstacles, thereby improving route selection efficiency and reducing data redundancy and calculation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AGV route re-planning is performed when obstacles are detected, then material transportation can continue, but time cost increases and production line efficiency decreases
Solution Approach 1:
The system pre-calculates and stores multiple alternative paths between different workstations before obstacles are detected. When an obstacle is detected, the system immediately switches to a pre-calculated alternative path without performing time-consuming re-planning calculations, thus maintaining transportation continuity while minimizing time loss.
Solution Approach 2:
The system dynamically selects among multiple pre-calculated paths based on real-time obstacle detection. The path selection is adjusted dynamically according to the current workstation positions and detected obstacles, allowing the AGV to adapt its route without重新启动 the entire path planning process.
2Productivity
If multiple alternative paths are pre-calculated for all workstation combinations, then route selection efficiency improves, but data redundancy and calculation cost increase
Solution Approach 1:
Instead of uniformly calculating all possible paths between all workstation pairs, the system identifies and prioritizes calculations for paths that are more likely to be used based on the specific production layout and frequent transportation needs. This local optimization reduces the overall data volume while maintaining efficiency for critical routes.
Solution Approach 2:
The system calculates a sufficient number of alternative paths to handle typical obstacle scenarios without calculating every theoretically possible path. This partial action approach provides enough alternatives for practical use while avoiding the excessive data generation that would result from exhaustive path calculation.
Data Source
AI summary
An industrial internet of things based on identification of material transportation obstacles, a control method and a storage medium are provided. The industrial internet of things comprises a service platform, a management platform and a sensor network platform which are connected in sequence, wherein the management platform comprises: an obtaining module, a selection module, classification module, calculation module, a planning module, a control module and a communication module. According to the industrial Internet of Things based on identification of material transportation obstacles, the control method and the storage medium, the selection efficiency of a new route in the event of material transportation obstacles can be effectively improved by classifying, planning and calculating intermediate paths. In addition, the data redundancy is low, the calculation cost and the time cost are low, and the impact of material transportation obstacles on material loading on an ultra-large-scale production line is reduced.