Semantic Gesture Sensing Control for Autonomous Crowd Reconfiguration
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Solution Overview
Problem
Existing systems for crowd control and information dissemination in dynamic environments require manual intervention and lack efficient, autonomous solutions for reconfiguration and semantic data processing.
Innovation Solution
A robotic semantic system comprising smart posts equipped with sensors, transceivers, and modular components that can autonomously infer and apply semantic rules for crowd control, information dissemination, and environmental adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for crowd control and information dissemination, then system reliability is maintained through human judgment, but productivity is reduced due to continuous manpower requirements
Solution Approach 1:
The smart post system performs self-service through autonomous semantic inference and automatic reconfiguration capabilities. The system processes sensor data, infers semantic meanings, and adjusts crowd control configurations without human intervention, allowing it to serve itself in managing dynamic environments
Solution Approach 2:
The system applies preliminary action by pre-configuring semantic rules and response protocols in advance. When events are detected, the system can immediately execute pre-planned reconfiguration actions based on semantic inference, eliminating the need for real-time human decision-making
2Adaptability or versatility
If fixed crowd control configurations are used, then device complexity is reduced and ease of operation is improved, but adaptability is worsened due to inability to reconfigure for dynamic events
Solution Approach 1:
The system implements dynamics by transitioning from fixed crowd control configurations to dynamic, adjustable arrangements. The smart posts can automatically reposition and reconfigure barriers based on real-time semantic inference about crowd behavior and environmental conditions
Solution Approach 2:
The crowd control system is segmented into independent smart post units that can operate autonomously and be individually reconfigured. This modular segmentation allows the system to adapt to dynamic events by adjusting specific segments without reconfiguring the entire system
3Loss of time
If semantic data processing is performed manually, then measurement precision is maintained through human analysis, but loss of time increases due to delayed information processing
Solution Approach 1:
The system replaces manual semantic analysis with automated computational processing. Sensors and processors substitute human cognitive functions by automatically inferring semantic meanings from raw data, eliminating time delays associated with manual information processing while maintaining accuracy through algorithmic consistency
Data Source
AI summary
A sensing control system includes a sensing controller comprising a memory storing a plurality of semantic identities and a processor in communication with the memory, at least one transceiver and at least one sensor. The sensing controller is configured to receive via the wireless transceiver and store in memory at least one semantic profile from a mobile device localized at an endpoint, the semantic profile comprising a set of configured gestures and semantic identities. Based on the semantic profile and inputs from the at least one sensor the sensing controller infers a plurality of semantics associated with gestures by a first person and/or second person and applies them based on a designated manipulation priority.