AI Robot Fleet Control for Dynamic Crowd-Density Guidance
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Solution Overview
Problem
In crowded spaces like airports, existing robot systems struggle to efficiently provide guidance services as they are often limited to specific zones and cannot effectively redistribute to areas of high user density.
Innovation Solution
A control system using artificial intelligence that receives user information from multiple zones, calculates density, determines priorities based on average densities, and controls robot movement to areas of high demand, ensuring efficient guidance service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots are located only in allocated zones, then device complexity is reduced and control is simplified, but service coverage and responsiveness to crowded areas deteriorate
Solution Approach 1:
The patent implements dynamic zone allocation where robots can move between zones based on real-time user density. The control system continuously monitors user distribution and dynamically adjusts robot locations and service zones, transforming the static zone allocation into a dynamic adaptive system that responds to changing conditions.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring user density in various zones and using this information to control robot movement. The control system receives real-time data about user distribution and adjusts robot positions accordingly, creating a closed-loop control system that adapts to changing conditions.
2Reliability
If multiple robots are deployed to crowded areas, then service quality improves, but system complexity and coordination difficulty increase
Solution Approach 1:
The control system acts as an intermediary that coordinates multiple robots centrally. Rather than having robots communicate directly with each other, the control system receives user density information and dispatches appropriate numbers of robots to specific zones, simplifying the coordination complexity through centralized mediation.
Solution Approach 2:
The service area is segmented into multiple zones with different user density levels, and robots are assigned to specific zones based on demand. This segmentation allows the system to manage multiple robots independently in different zones, reducing overall coordination complexity while maintaining service quality.
3Productivity
If robots remain in fixed positions, then system stability is maintained, but responsiveness to changing user density deteriorates
Solution Approach 1:
The system transitions from static fixed-position robot deployment to dynamic position adjustment based on real-time user density monitoring. Robots can move between zones as conditions change, enabling the system to respond quickly to crowded areas while maintaining operational stability through controlled, rule-based movement.
Data Source
AI summary
A control system for controlling a plurality of robots using artificial intelligence includes a communication unit configured to receive user information of each of a plurality of unit zones in which the plurality of robots is disposed, and a processor configured to calculate a plurality of densities respectively corresponding to the plurality of unit zones based on the user information of each unit zone, calculate average densities of a plurality of group zones using the plurality of calculated densities, determine respective priorities of the plurality of group zones based on the calculated average densities, and control movement of one or more of the plurality of robots based on the determined priorities.