Drone Reach-Envelope Detection for Ride Interference Inspection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional inspection and testing methods for amusement park attractions to identify undesirable proximity between movable ride vehicle assemblies and other components are cumbersome, time-consuming, inaccurate, and expensive.
Innovation Solution
An interference detection assembly using drones equipped with sensors and processors to detect overlaps between a ride vehicle's reach envelope and additional components by capturing sensor data and generating notifications for remedying overlaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inspection and testing methods are used to identify undesirable proximity between ride vehicle assemblies and other components, then the inspection process can be performed, but the process becomes cumbersome, time-consuming, and expensive
Solution Approach 1:
The patent replaces traditional mechanical inspection methods with an optical sensing system. Sensors mounted on mobile platforms (vehicles, drones, robots) capture images and sensor data of the attraction environment, which are then processed by machine learning models to detect interferences. This substitution of mechanical measurement with optical sensing and computational analysis enables faster, more accurate, and automated inspection without requiring manual measurement tools or procedures.
Solution Approach 2:
The patent introduces mobile platforms (vehicles, drones, or robots) as intermediaries to carry sensors through the attraction environment. These mobile carriers serve as mediators between the fixed attraction components and the inspection system, enabling flexible data collection from multiple positions and angles. The mobile platform bridges the gap between stationary sensors and moving ride vehicles, allowing comprehensive coverage of the operational space.
2Reliability
If traditional inspection methods are used, then the process can be completed, but it becomes cumbersome and expensive
Solution Approach 1:
The patent creates a universal inspection system that can detect multiple types of interferences (spatial conflicts, timing conflicts, reach envelope violations) using the same mobile platform and sensor suite. The machine learning model is trained to identify various interference types across different attraction configurations, making the system adaptable to roller coasters, dark rides, water rides, and other attraction types without requiring specialized equipment for each scenario.
Solution Approach 2:
The inspection system performs self-validation through machine learning model training and testing. The system automatically processes sensor data, identifies potential interferences, and generates reports without requiring manual verification. The machine learning model learns from training data to improve its detection accuracy autonomously, reducing the need for expert human inspectors while maintaining high reliability.
3Productivity
If traditional inspection methods are used, then inspection can be performed, but accuracy is insufficient
Solution Approach 1:
The patent enables continuous inspection by mounting sensors on mobile platforms that move continuously through the attraction environment during normal operations or testing. Rather than performing discrete, periodic inspections, the system continuously collects data as the mobile platform traverses the space, ensuring no interference goes undetected. This continuous data collection maintains high productivity while achieving comprehensive coverage for accurate detection.
Solution Approach 2:
The system implements feedback through machine learning model training and validation. Sensor data from inspections feeds into the machine learning model, which learns from detected interferences and improves its detection algorithms. The system provides feedback reports to operators about detected interferences, enabling corrective actions. This closed-loop feedback mechanism continuously improves detection accuracy while maintaining efficient operation.
Data Source
AI summary
An amusement park attraction system includes one or more travel paths configured to guide one or more ride vehicle assemblies thereon. The amusement park attraction system also includes an interference detection assembly comprising one or more drones, a sensor assembly disposed on the drone, one or more memories, and one or more processors. The one or more memories include instructions stored thereon. The one or more processors are configured to execute the instructions to control a path of the one or more drones relative to the one or more travel paths, receive sensor data from the sensor assembly and corresponding to an environment surrounding the one or more drones, detect an overlap between a reach envelope corresponding the one or more ride vehicle assemblies and at least one additional component of the amusement park attraction system based on the sensor data, and generate one or more notifications indicative of the overlap.