Robot Resource Planning for Service-Specific Accessory Units
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Solution Overview
Problem
Existing systems fail to provide a quantitative assessment of the resources needed for autonomous mobile robots and their accessory units based on the location of use and service requirements.
Innovation Solution
An information providing system and method that calculates resource information for autonomous mobile robots and accessory units by receiving input data, including information about the location of operation, type of service, and accessory units, using a machine learning model to determine the necessary resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing systems are used for autonomous mobile robots, then the robot can operate with basic functionality, but the system cannot provide quantitative assessment of necessary resources based on location and service requirements
Solution Approach 1:
The system segments resource assessment into distinct components: robot unit information, accessory unit information, location information, and service type information. Each component is processed separately through the machine learning model to generate comprehensive resource information, enabling quantitative assessment without overwhelming system complexity
Solution Approach 2:
A machine learning model serves as an intermediary between input information (robot specifications, location data, service requirements) and output resource information. This intermediary processes multiple input parameters simultaneously to generate quantitative resource assessments, bridging the gap between basic robot operation and comprehensive resource planning
2Measurement precision
If comprehensive input information is collected for resource calculation, then accurate resource assessment is achieved, but information processing complexity increases
Solution Approach 1:
The machine learning model transforms multiple input parameters (robot unit specifications, accessory unit types, location characteristics, service requirements) into a unified output parameter set representing comprehensive resource information. This parameter transformation approach maintains high measurement precision while managing processing complexity through automated computational methods
Data Source
AI summary
The information providing system according to the present disclosure inputs input information including information on a plurality of robot accessory units that enable a mobile robot to execute a plurality of different services by being used in combination with a mobile robot that moves autonomously, information on a place where the mobile robot operates, and information on a type of a required service. On the basis of the input information, the information providing system performs a calculation process of calculating resource information, which is information related to a resource amount of the mobile robot and the plurality of robot accessory units required for the service, and outputs a result calculated in the calculation process.