Drone Racing Gate Sensing for Real-Time Adaptive Gameplay
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Solution Overview
Problem
Existing drone gaming and racing systems lack dynamic and adaptive elements that can adjust gameplay or race dynamics in real-time based on user skill levels, leading to a less engaging and challenging experience.
Innovation Solution
The integration of sensor technology, microprocessors, and machine learning models in drone gaming and racing systems to track drone movement, process data, and adjust gameplay or race dynamics in real-time, incorporating features like lighting effects, sound effects, and adaptive difficulty levels based on user skill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor technology and machine learning models are integrated to dynamically adjust gameplay in real-time, then user engagement and challenge adaptability are improved, but system complexity and cost increase
Solution Approach 1:
The gate structure is designed to perform multiple functions: it serves as a physical obstacle for drones to navigate, a sensing station with RFID readers and cameras to detect drone passage and capture images, and a computing node with integrated processors to analyze data and trigger visual effects. This multi-functionality reduces the need for separate dedicated components for each function.
Solution Approach 2:
The system implements a hierarchical nested structure where RFID sensors are embedded within the gate, cameras are positioned around the gate structure, processors are integrated into the gate control units, and multiple gates are arranged within the larger play area. This nesting allows compact integration of multiple subsystems.
2Measurement precision
If multiple sensors and processing units are deployed at each gate to track drones and provide real-time feedback, then measurement precision and real-time tracking are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent combines RFID sensing, camera imaging, and processing capabilities into integrated gate units. The RFID reader and camera work together to detect and track drones, while the integrated processor analyzes data from both sensors simultaneously. This merging reduces the need for separate dedicated components for each sensing and processing function.
Solution Approach 2:
Each gate is equipped with its own processor that autonomously analyzes sensor data and determines when a drone has passed through, triggering visual effects without requiring constant communication with a central controller. This self-service capability reduces system complexity and improves real-time responsiveness.
Data Source
AI summary
The present disclosure provides a system for processing sensor data and providing user feedback. The system includes at least two sensors configured to sense sensor data for an object near a gate. A microprocessor is configured to determine at least one parameter for the object based on the sensor data. A control system is configured to output a graphical user interface depicting data related to the object based on the at least one parameter. The system enables real-time tracking and analysis of object movement through the gate, providing instant feedback for various applications such as sports training, performance analysis, and interactive learning experiences.