AI Path Planning for Cooperative Warehouse Mobile Robots
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Solution Overview
Problem
Current methods for transporting items within a warehouse are inefficient, leading to prolonged item movement times due to the need for multiple robots to transport items individually or make multiple trips, lacking a systematic approach to optimize movement paths.
Innovation Solution
A computer-implemented method using artificial intelligence to determine an optimal movement path for autonomous mobile robots, either individually or in cooperation, by building and training a model based on historical data and simulations, which can create continuous or combined movement paths to minimize transport time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple mobile robots transport items individually or make multiple trips, then the system can handle item transportation, but the item movement time is prolonged and efficiency is reduced
Solution Approach 1:
The patent combines multiple mobile robots into a cooperative team that works together to transport items. The system creates coordinated movement paths where robots can transport items simultaneously rather than sequentially, and can form continuous conveyor belt paths by connecting multiple robots, thereby reducing total item movement time and improving productivity
Solution Approach 2:
The system performs preliminary actions by building and training an AI model with historical data before actual item transportation. The model pre-determines optimal movement paths and robot assignments based on simulated scenarios, allowing the system to quickly execute efficient transportation routes without real-time calculation delays
2Extent of automation
If autonomous mobile robots are used without systematic path optimization, then robots can navigate independently, but the movement paths are not optimized leading to prolonged transport times
Solution Approach 1:
The system implements feedback by using historical transportation data to train the AI model. The model learns from past performance and continuously improves path determination accuracy. Historical times for movement along various paths are fed back into the model to refine future path selections and optimize transport times
Solution Approach 2:
The system performs preliminary path optimization by simulating various movement scenarios and training the AI model before actual transportation operations. The model pre-determines optimal paths based on simulated outcomes and historical data, so that when real transportation occurs, the already-optimized paths can be executed without real-time optimization delays
3Productivity
If mobile robots cooperate to create continuous movement paths with connected conveyor belts, then item transport efficiency is improved, but the system complexity increases
Solution Approach 1:
The AI-based path determination system acts as an intermediary that manages the complexity of coordinating multiple robots. The model receives transportation requests and outputs optimized movement paths and robot assignments, thereby mediating between the simple need for item transport and the complex reality of multi-robot coordination without requiring direct complex interactions between robots themselves
Data Source
AI summary
A computer-implemented method, system and computer program product for transporting items from one location to another location, such as in a warehouse. A model is built and trained to determine an optimal movement path for transporting items using mobile robots either individually or in cooperation with other mobile robots. After training the model to determine an optimal movement path for transporting items, simulations of mobile robots transporting items from a source location to a target location using various movement paths are performed. An optimal movement path for transporting such items from the source location to the target location is identified using the trained model based on the simulated movement paths, the historical times for the movement of the items using various movement paths, the number of items to be transported and the properties of the items. Available mobile robots are then organized to implement the identified optimal movement path.