AI Robot Route Planning Using Predicted Crowd Density
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Solution Overview
Problem
Artificial intelligence robots in airports and multiplexes struggle to effectively manage guidance services during high people density situations, as they are typically confined to allocated areas and cannot actively respond to increased demand.
Innovation Solution
An AI server predicts future density in control spaces using image data from cameras, calculates current and future densities, determines priority areas, and directs robots to move to areas with expected high density based on crowd movement patterns and additional information like schedule and facility usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If robots are confined to allocated areas, then device management is simplified, but service coverage and responsiveness to high density situations deteriorate
Solution Approach 1:
The robot's operational area is transformed from a static allocated zone to a dynamic region that changes based on real-time crowd density predictions. The server continuously updates the robot's target location by predicting future density distributions, allowing the robot to adaptively move to areas where guidance services are most needed while maintaining manageable deployment through centralized control.
Solution Approach 2:
The system performs preliminary density prediction to anticipate future crowd distribution before the robot needs to move. By calculating future density based on current density and movement patterns, the server proactively determines optimal robot positions in advance, enabling the robot to be positioned where services will be needed rather than merely reacting to current conditions.
2Stability of the object's composition
If robots remain stationary in allocated areas, then operational stability is maintained, but responsiveness to changing crowd density deteriorates
Solution Approach 1:
The system implements a feedback loop where the server continuously receives crowd density information, predicts future density distributions, and adjusts robot positions accordingly. This closed-loop control maintains operational stability through systematic decision-making while ensuring service reliability by continuously adapting to changing conditions based on predicted crowd patterns.
Solution Approach 2:
The server performs preliminary calculations of future crowd density before directing robot movement. This advance prediction allows the system to maintain stable, deliberate positioning decisions rather than reactive movements, ensuring that robots are reliably positioned in areas where guidance services will be needed based on anticipated crowd flow patterns.
3Adaptability or versatility
If multiple robots are deployed throughout the control area, then service coverage is improved, but system complexity and control difficulty increase
Solution Approach 1:
The server performs multiple functions including crowd density monitoring, future density prediction, robot position optimization, and route determination through a single integrated system. This multi-functional approach improves service coverage by coordinating multiple robots while avoiding the complexity of separate control systems for each robot, as the server centrally manages all robots based on unified density predictions.
Solution Approach 2:
The server performs preliminary determination of optimal positions for all robots based on predicted future density distributions. By calculating target locations for multiple robots in advance based on anticipated crowd patterns, the system achieves comprehensive service coverage while simplifying control, as all robot positions are coordinated through a single predictive model rather than complex real-time interactions between multiple independent controllers.
Data Source
AI summary
An artificial intelligence server for determining a route of a robot includes a communication unit and a processor. The communication unit is configured to receive image data for a control area from the robot or a camera installed inside the control area. The processor is configured to calculate a current density for the control area from the image data, calculate a future density for the control area using the calculated current density, determine a priority for each of group areas included in the control area based on the calculated future density, and determine the route of the robot based on the determined priority.