Neural-Guided Drone Inspection for Passive Sensor Energization
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Solution Overview
Problem
Conventional drones lack the ability to adapt to unplanned changes in flight paths, react to unexpected obstacles, and collect data from passive sensors that are not within their line of sight, necessitating human intervention and limiting the effectiveness of inspections on large-scale structures.
Innovation Solution
A drone inspection system equipped with a neural compute engine, energy transfer module, and passive sensors with energy harvesters, utilizing optical flow sensors and inertial navigation to navigate and energize sensors, enabling precise data collection and obstacle avoidance through neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional GPS navigation is used for drone flight path control, then the drone can follow preprogrammed flight paths, but the drone cannot adapt to unplanned changes or obstacles in the operating environment
Solution Approach 1:
The patent implements a neural network-based feedback system that continuously monitors sensor data from the operating environment and automatically adjusts the drone's flight path in real-time. The neural network processes data from cameras, LIDAR, and other sensors to detect obstacles and unplanned changes, then feeds back control signals to modify the flight path without human intervention, enabling adaptation while maintaining manageable system complexity through intelligent automation.
Solution Approach 2:
The patent replaces conventional GPS-based mechanical navigation with a neural network-based intelligent navigation system. Instead of relying solely on preprogrammed GPS waypoints, the system uses machine learning algorithms to interpret sensor data and make autonomous navigation decisions, substituting rigid mechanical control with flexible intelligent control that can adapt to unexpected conditions.
2Reliability
If the drone maintains a safe distance from the structure, then collision avoidance is achieved, but sensors cannot inspect regions requiring close approach
Solution Approach 1:
The patent implements dynamic flight path adjustment where the drone's distance from the structure is continuously optimized based on real-time conditions. The neural network analyzes sensor data to determine the optimal approach distance, allowing the drone to dynamically adjust between maintaining safe distances for collision avoidance and approaching closely for high-quality inspection data collection, rather than maintaining a fixed distance.
Solution Approach 2:
The drone system performs self-service by autonomously determining and executing optimal flight paths that balance safety and inspection quality. The neural network enables the drone to make its own decisions about when to approach closely for detailed inspection and when to maintain distance for safety, without requiring constant human operator intervention to manage the trade-off between collision avoidance and inspection quality.
3Productivity
If the drone operates autonomously without operator control, then operational efficiency increases, but the drone cannot react to anomalies or unexpected conditions
Solution Approach 1:
The patent implements comprehensive feedback loops where sensor data from the operating environment is continuously monitored and fed back to the neural network for real-time analysis. When anomalies or unexpected conditions are detected, the system automatically adjusts its operation while maintaining autonomous efficiency. The feedback mechanism enables the drone to react to anomalies without requiring operator intervention, preserving both productivity and adaptability.
Solution Approach 2:
The patent replaces human operator control with neural network-based autonomous control that can react to anomalies. The machine learning system substitutes human decision-making with automated intelligent processing of sensor data, enabling the drone to maintain high operational efficiency while simultaneously reacting to unexpected conditions through algorithmic analysis and autonomous response.
4Use of energy by moving object
If passive sensors are used on the structure, then power consumption is reduced, but the sensors require external energization to collect data
Solution Approach 1:
The patent introduces an energy transfer module as an intermediary between the drone and passive sensors. This module wirelessly transfers energy to the passive sensors, enabling them to operate without internal power sources. The intermediary energy transfer system resolves the contradiction by providing the necessary power to sensors while maintaining their passive, low-power-consumption characteristics and avoiding the complexity of hardwired power connections.
Solution Approach 2:
The patent replaces physical power connection mechanisms with wireless energy transfer technology. Instead of using cables or direct electrical connections to power sensors, the system uses electromagnetic or other wireless energy transfer methods, substituting mechanical power delivery with field-based energy transfer that reduces complexity while enabling passive sensor operation.
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 autonomous, efficient data collection from passive sensors, creating a digital twin of the structure with accurate structural condition data, allowing for predictive maintenance and reducing power consumption, thus enhancing inspection capabilities.
Implementation Method 1
an energy transfer module targetable to energize an energy harvester such that when energized, the energy harvester energizes a passive sensor
Implementation Method 2
leverages optical flow sensors and inertial navigation for precise flight path adjustments
Implementation Method 3
leverages optical flow sensors and inertial navigation for precise flight path adjustments
Data Source
AI summary
A drone system for collecting structural condition data about a structure having an array of sensors disposed at various locations on the structure and methods of using such a drone system are disclosed herein. The drone inspection system leverages neural networks to calculate a drone flight path to classify the location of passive sensors and calculate a drone flight path to collect structural condition data about the structure using line of sight sensors for digital twin generation. Some of the sensors disposed on the structure may be passive sensors that comprise energy harvesters and must be energized to report the structural collection data to the drone. The drone inspection system may comprise an energy transfer module for energizing the passive sensor via the energy harvester.