Robot Navigation Using Crowd Flow Analysis in Dense Pedestrian Spaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional robot navigation systems are ineffective in densely crowded spaces as they only track a small number of immediate obstacles, failing to account for overall crowd density and movement direction, leading to inefficient or stalled navigation.
Innovation Solution
A system incorporating a traffic analysis unit with sensors like high-resolution cameras, LIDAR, and ultrasonic sensors to monitor pedestrian traffic, providing a third-person perspective and processing data to determine crowd density and movement patterns, allowing the robot to select efficient navigation routes based on traffic analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional robot navigation systems track only a small number of immediate obstacles, then the system complexity is reduced, but the navigation effectiveness in densely crowded spaces deteriorates
Solution Approach 1:
The patent transitions from tracking individual obstacles in 2D space to analyzing crowd density and flow patterns in a comprehensive spatial field. The system divides the workspace into multiple zones and evaluates density metrics across these zones, adding a dimensional layer of crowd analysis beyond simple obstacle detection. This enables the robot to navigate densely crowded spaces by understanding overall crowd dynamics rather than just immediate obstacles.
2Loss of information
If the robot uses advanced controllers to track multiple pedestrians, then the navigation awareness is improved, but the processing complexity and computational load increase
Solution Approach 1:
The patent extracts essential crowd navigation information from complex sensor data by focusing on density metrics and flow patterns rather than tracking every individual pedestrian. The system divides the workspace into zones and calculates density values for each zone, extracting only the critical information needed for navigation decisions. This reduces computational load while maintaining comprehensive crowd awareness.
Solution Approach 2:
The system performs partial tracking by monitoring crowd density and flow patterns in key zones rather than exhaustively tracking all pedestrians. By focusing on aggregate crowd behavior in divided zones rather than individual trajectories, the system achieves sufficient navigation awareness with reduced processing complexity.
3Speed
If the robot navigates through crowded areas without crowd density analysis, then the navigation speed is maintained, but the safety and efficiency in densely populated zones deteriorate
Solution Approach 1:
The system performs preliminary crowd density analysis by dividing the workspace into zones and pre-calculating density metrics before the robot makes navigation decisions. This advance analysis of crowd patterns in different zones enables the robot to plan safe and efficient routes beforehand, maintaining navigation speed while ensuring safety in densely populated areas.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables robots to navigate safely and efficiently through crowded areas by understanding overall crowd dynamics, avoiding dense zones and selecting optimal routes, improving their ability to reach targets in complex, uncontrolled environments.
Implementation Method 1
sensors like high-resolution cameras, LIDAR, and ultrasonic sensors
Implementation Method 2
sensors like high-resolution cameras, LIDAR, and ultrasonic sensors
Data Source
AI summary
A system for improving navigation of robots in a space with a plurality of pedestrians or other movable objects or obstacles. The system includes a traffic analysis assembly that has a traffic sensor(s) sensing movement of the obstacles in the space. The traffic analysis assembly further includes a processor running a flow module that processes (such as the Gunnar-Farneback optical flow algorithm) output from the traffic sensor to generate traffic analysis results, which include density values for the obstacles in the space and motion information for the obstacles in the space (e.g., speed and direction). The system includes a robot with a controller running a navigation module selecting a navigation route between a current location of the robot and a target location in the space using the traffic analysis result. The workspace is configured such that the obstacles such as pedestrians have unregulated flow patterns in the space.