IIoT AGV Path Planning for Fast Obstacle Rerouting
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Solution Overview
Problem
On super-large-scale production lines, obstacles on AGV routes can significantly affect the transportation efficiency, requiring time-consuming re-planning of routes which impacts production line efficiency.
Innovation Solution
An Industrial Internet of Things system is implemented, comprising a user platform, service platform, management platform, and sensor network platform, which uses a second iteration approach to determine an optimal AGV path by calculating connected node and edge transition probabilities based on obstacle situations, updating pheromone information, and selecting the best path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional route re-planning is performed when obstacles are detected on AGV routes, then the AGV can avoid obstacles and continue transportation, but the route re-planning process consumes significant time and reduces production line efficiency
Solution Approach 1:
The system pre-calculates and stores multiple alternative routes between different nodes in the production workshop before obstacles appear. When an obstacle is detected, the AGV immediately switches to a pre-computed alternative route without performing time-consuming real-time re-planning, thus resolving the contradiction between obstacle avoidance reliability and re-planning time loss.
Solution Approach 2:
The system dynamically adjusts the route selection based on real-time obstacle detection while maintaining a library of pre-computed routes. The route planning transitions from static pre-planning to dynamic adaptive selection, allowing the AGV to quickly respond to obstacles by choosing from pre-prepared alternative paths, thereby reducing re-planning time while ensuring reliable obstacle avoidance.
2Productivity
If multiple alternative routes are pre-calculated and stored for AGV navigation, then route selection efficiency is improved when obstacles occur, but the system complexity and data storage requirements increase
Solution Approach 1:
The production workshop is divided into multiple nodes and segments, with alternative routes pre-calculated for each segment rather than for the entire workshop. This segmentation reduces the overall data storage requirements and system complexity while maintaining high route selection efficiency, as the AGV only needs to select from alternatives in the current segment rather than evaluating all possible routes throughout the entire workshop.
Solution Approach 2:
The system implements local alternative route planning for each specific segment or node where obstacles may occur, rather than maintaining global alternative routes for the entire workshop. This local quality approach reduces the overall system complexity and data storage requirements while ensuring that alternative routes are available exactly where needed, thereby maintaining high route selection efficiency without proportionally increasing system complexity.
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 solution effectively improves the efficiency of selecting new routes in the presence of obstacles, reduces data redundancy and calculation costs, and minimizes the impact of obstacles on material loading in ultra-large-scale production environments.
Implementation Method 1
updating pheromone of all the nodes and pheromone of all the edges, the pheromone being mark information left by the decision-making entity when passing the second transition route, the pheromone of the nodes being used to determine the connected node transition probability, and the pheromone of the edges being used to determine the first edge transition probability
Data Source
AI summary
An industrial internet of things for determining a target automatic guided vehicle (AGV) path, 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. The management platform comprises: an obtaining module configured to obtain an AGV route layout of a target workshop through the sensor network platform, and generate a basic layout directed graph according to the AGV route layout; and a determination module configured to determine, based on the basic layout directed graph, an optimal AGV path through a second iteration approach; generate a target AGV path at least based on the optimal AGV path; and control an AGV to carry out material transportation according to the target AGV path, and send the target AGV path to the user platform for display to a user.