Airport Robot Control Using User Density and Feature Priority
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Solution Overview
Problem
Airport robots are limited in number due to high costs, leading to inefficient navigation services and difficulties in providing adequate guidance in densely populated areas, as they often lack the capability to dynamically adjust their movements based on user density and features.
Innovation Solution
A control system that uses AI processing to determine the density of users in different zones, extracts user features such as sex, age, and service history, and assigns priorities to zones, allowing robots to optimize their movement routes and allocate services effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of airport robots is increased to improve navigation service coverage, then service quality improves, but system cost increases
Solution Approach 1:
The patent implements dynamic robot allocation where robots change their operating zones based on real-time user density measurements. The processor continuously monitors user density in different airport zones and redistributes robots to high-density areas, allowing the system to maintain high service coverage with fewer robots by optimizing their spatial-temporal distribution patterns.
Solution Approach 2:
The system changes the operational parameters of robots by adjusting their target zones based on user density thresholds. When user density in a zone exceeds a predetermined threshold, the processor redirects robots to that zone, effectively changing the robots' service parameters dynamically rather than maintaining fixed assignments.
2Productivity
If robots move to all areas of the airport to provide navigation service, then service coverage improves, but service efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-positioning robots in zones where user density is expected to be high or where services are most needed. The processor analyzes user density patterns and proactively redirects robots to potential high-demand areas before congestion occurs, reducing response time while maintaining coverage.
Solution Approach 2:
Robots dynamically adjust their movement patterns and destination selection based on real-time user density feedback. Instead of following fixed routes or serving all areas equally, robots concentrate in high-density zones and quickly respond to density changes, optimizing the trade-off between coverage and response time.
3Productivity
If similar destination robots concentrate in specific areas, then local service density improves, but adaptability to other areas deteriorates
Solution Approach 1:
The system changes the destination parameters of robots dynamically based on user density measurements. When a zone's user density exceeds thresholds, the processor modifies the destination parameters of available robots to redirect them to that zone, even if their original destinations differed. This maintains local service density while preserving overall system adaptability through parameter flexibility.
Solution Approach 2:
The processor continuously monitors user density in all airport zones and uses this feedback to adjust robot destination assignments. The feedback loop ensures that robots are redirected to areas needing service while maintaining the ability to adapt to changing conditions, preventing permanent concentration in single areas.
Data Source
AI summary
Disclosed is a control system to control a robot. The control system to control the robot according to an aspect of the present disclosure includes a transceiver and a processor. The transceiver receives information of a user within each unit zone, wherein a plurality of robots is disposed in a zone. The processor is configured to determine a density for each respective unit zone from among the plurality of zones, determine an average density for each respective group zone from among the plurality of group zones based on the determined density, determine a priority for each group zone based on the respective determined average density, and control movements of one or more robots based on the determined priority, wherein the controlling of the movements is performed by extracting a feature from the user based on machine learning and setting the priority based on the extracted feature of the user.