Multi-Agent Route Planning With Local Control Area Optimization
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Solution Overview
Problem
Existing methods for calculating the movement routes of multiple robots in a warehouse or factory environment require extensive manual passage setting, leading to high engineering costs and difficulty in real-time optimization as the number of robots increases, resulting in inefficient and time-consuming calculations.
Innovation Solution
An agent management system that divides the management area into control areas, with local computing sections determining moving routes for agents within each area, using model predictive control to optimize routes and prevent collisions while reducing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If detailed passage setting is implemented to improve transport efficiency, then transport efficiency is improved, but engineering cost increases
Solution Approach 1:
The management area is divided into multiple control areas, each handled by a separate local computing section. This segmentation allows each local computing section to independently optimize routes within its own control area without requiring detailed passage settings across the entire facility, thereby maintaining transport efficiency while reducing overall system complexity and engineering costs.
2Quantity of substance
If the number of robots is increased to improve transport capacity, then transport capacity is improved, but calculation time increases
Solution Approach 1:
By dividing the management area into control areas and assigning local computing sections to each, the system can handle multiple robots in parallel across different control areas. This segmentation reduces the calculation time required to manage increasing numbers of robots, as each local computing section independently processes routes for robots within its own area rather than calculating all routes centrally.
Solution Approach 2:
Each local computing section focuses on optimizing routes only within its own control area rather than calculating complete routes across the entire facility. This partial action approach reduces the computational burden and allows the system to scale to handle more robots without proportionally increasing calculation time.
3Manufacturing precision
If centralized route calculation is used to ensure optimal routes, then route optimality is improved, but calculation speed decreases
Solution Approach 1:
The system divides the facility into control areas with local computing sections handling route optimization for each area independently. This segmentation enables faster local optimization decisions while maintaining overall route quality, as each local computing section can quickly calculate routes within its own area without waiting for centralized calculations.
Solution Approach 2:
Each local computing section is empowered to make route optimization decisions locally within its control area, allowing for faster response times and real-time route adjustments. This local quality approach maintains sufficient route optimality for local operations while dramatically improving calculation speed compared to centralized control.
Data Source
AI summary
Provided are an agent management system and method capable of calculating, in real time, a route plan for efficiently moving a large number of agents. This agent management system is characterized by comprising agents that can move within a management area, and local calculation units that determine movement routes for moving the agents from initial positions to target positions, wherein the management area is divided into a plurality of control areas, and wherein each local calculation unit is provided for a respective control area, determines a movement route for the agents within the control area, and assigns the movement route to these agents.