Mobile Robot Scheduling Using Image-Based Work Region State Recognition
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Solution Overview
Problem
Inefficient scheduling of intelligent mobile robots in warehouses leads to prolonged waiting times and congestion due to a lack of real-time information about the state of working regions, such as empty cargo spaces and pallets, affecting their operational efficiency.
Innovation Solution
A scheduling system that uses a processor to analyze images from an image acquisition device to recognize the state of working regions and send scheduling instructions to intelligent mobile robots, directing them to suitable regions based on their tasks, thereby optimizing their movement and reducing waiting times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple intelligent mobile robots operate in warehouses without real-time scheduling, then robots can perform transporting and unloading tasks independently, but robots experience prolonged waiting times and congestion due to lack of real-time information about working region states
Solution Approach 1:
The scheduling system continuously acquires images of working regions and updates the state information of each region in real-time. When a robot queries for available working regions, the system responds with current state information, enabling dynamic scheduling decisions that reduce waiting time and improve operational efficiency.
Solution Approach 2:
The scheduling system acts as an intermediary between multiple intelligent mobile robots and the working regions. It receives query requests from robots, analyzes the current state of working regions based on image data, and returns suitable working region recommendations, thereby coordinating robot activities and reducing congestion.
2Measurement precision
If the scheduling system analyzes images in real-time to recognize working region states, then scheduling accuracy and timeliness improve, but system complexity and processing requirements increase
Solution Approach 1:
The scheduling system extracts only the necessary state information from images (such as whether working regions are occupied or available) rather than processing and analyzing all image details. This selective extraction approach maintains recognition accuracy while reducing processing complexity and computational requirements.
Solution Approach 2:
The working area is divided into multiple discrete working regions, each with a defined state (occupied or available). The image analysis process segments the overall scene into individual region assessments, making the complexity manageable by handling each region independently rather than analyzing the entire workspace as a single unit.
3Loss of information
If the scheduling system monitors all working regions continuously, then real-time allocation accuracy improves, but information processing load and system resource consumption increase
Solution Approach 1:
The scheduling system pre-divides the working area into multiple working regions and establishes a framework for state monitoring before actual operations begin. This preliminary structuring allows the system to efficiently process information during operations by only analyzing changes in predefined regions rather than continuously scanning the entire workspace.
Solution Approach 2:
The system performs image analysis and state recognition on demand when robots query for working regions, rather than continuously monitoring all regions at maximum detail. This partial action approach provides sufficient real-time information for scheduling decisions while reducing overall processing load and resource consumption.
Data Source
AI summary
Disclosed is a scheduling system for an intelligent mobile robot. The scheduling system (10) comprises a processor (100). The processor (100) is configured to: obtain an image about at least one working region of a working table (102); receive a query request from at least one intelligent mobile robot (104); and analyze the obtained image in response to the query request to recognize a current state of each working region, and on the basis of the query request and the recognized current state of each working region, select a corresponding working region suitable for the at least one intelligent mobile robot (104) from the at least one working region, and send a corresponding scheduling instruction to the intelligent mobile robot (104) to instruct the intelligent mobile robot (104) to move to the selected corresponding working region. By using the scheduling system of the invention, intelligent mobile robots can be scheduled in a timely manner to travel to suitable working regions, such that the waiting time is saved, congestion is avoided, the scheduling efficiency is improved, and related operation services of the working table are effectively managed. Further comprised are a scheduling method and a non-transitory computer-readable storage medium.