Modular Smart Posts for Autonomous Crowd Guidance Reconfiguration
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Solution Overview
Problem
Existing robotic systems for crowd control and guidance lack the ability to dynamically reconfigure and adapt to changing environments and user needs, relying on manual intervention for setup and requiring multiple components that are not easily integrated for semantic augmentation and information processing.
Innovation Solution
A modular robotic system comprising smart posts with integrated power, sensor, and control modules that use semantic routes and inferences for autonomous reconfiguration, semantic augmentation, and adaptive information processing, enabling dynamic deployment and redeployment based on environmental conditions and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for setup and reconfiguration of robotic systems, then system reliability is maintained through human control, but productivity is reduced due to continuous manual operation requirements
Solution Approach 1:
The robotic system performs self-reconfiguration through autonomous semantic processing and decision-making. The system uses semantic routes and inferences to automatically adjust its configuration and behavior based on environmental conditions, eliminating the need for continuous manual intervention while maintaining adaptive responsiveness.
Solution Approach 2:
The system implements dynamic reconfiguration capabilities where robotic devices can automatically adjust their operational parameters, positions, and configurations in real-time based on changing environmental conditions and semantic analysis of the situation, enabling continuous adaptation without manual control.
2Adaptability or versatility
If multiple separate components are used for sensing, control, and actuation, then system versatility is improved through specialized functions, but device complexity increases due to integration requirements
Solution Approach 1:
The patent integrates sensing, control, and actuation functions into unified modular units that can operate independently yet coordinate through semantic communication. This merging reduces integration complexity while maintaining the versatility of specialized functions through modular design principles.
Solution Approach 2:
The system employs universal communication protocols and semantic processing frameworks that enable different specialized components to interact through a common language. This allows diverse functional modules to be integrated without increasing complexity, as they all communicate through standardized semantic routes and inferences.
3Adaptability or versatility
If fixed configuration robotic systems are used, then manufacturing precision is maintained through standardized designs, but adaptability is reduced when facing changing environmental conditions
Solution Approach 1:
The robotic system transitions from fixed to dynamic configuration, enabling automatic adaptation to changing environmental conditions through semantic processing. The system can reconfigure its operational parameters, positions, and behaviors in real-time based on environmental feedback while maintaining standardized hardware designs.
Solution Approach 2:
The system achieves adaptability by dynamically changing operational parameters such as position, speed, and configuration states rather than requiring physical reconfiguration of the hardware. This allows fixed manufacturing designs to exhibit variable behavior through parameter adjustment based on semantic environmental analysis.
Data Source
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AI summary
A robotic semantic system includes one or more smart robotic devices, which may be configured as a stack of modules including a mobility module and one or more sensor modules. A plurality of robotic modules is communicatively coupled to one another, and use variable semantic coherent inferences to allow the devices to perform semantic augmentation.