Neural-Network UAV Navigation for Obstacle and GPS Drift Adaptation
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Solution Overview
Problem
Conventional drones lack the ability to autonomously adapt to unplanned changes in their flight path, such as obstacles or GPS drift, and fail to react to anomalies in the environment, leading to inefficient and potentially unsafe inspections, especially in complex environments like large installations.
Innovation Solution
Equipping drones with a neural network (DINN) that analyzes real-time image data and sensor information to navigate and adjust flight paths, and collaborating with high-altitude pseudosatellite platforms (HAPSNN) for real-time airspace management and communication, enabling autonomous obstacle avoidance and efficient mission execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional drones follow preprogrammed flight paths with GPS navigation, then navigation simplicity is maintained, but the drone cannot adapt to unplanned changes such as obstacles or GPS drift
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks onboard the drone before mission execution. The neural network is trained with obstacle detection algorithms and flight path correction methods in advance, enabling the drone to autonomously adapt to unexpected conditions without requiring complex real-time decision-making infrastructure. This resolves the contradiction by preparing adaptive capabilities beforehand, maintaining navigation simplicity during operation.
Solution Approach 2:
The drone implements self-service through autonomous obstacle detection and flight path correction using onboard sensors and neural networks. The system automatically detects obstacles, calculates safe alternative paths, and executes corrections without human intervention or complex external control systems. This self-service capability provides adaptability while keeping the overall system architecture relatively simple.
2Extent of automation
If drones are controlled by operators within line of sight, then real-time control is achieved, but personnel presence and sustained attention are required at each inspection site
Solution Approach 1:
The patent replaces the mechanical control system requiring human operators with an autonomous neural network-based control system. The neural network processes sensor data and generates flight control commands autonomously, substituting human operators and sustained attention requirements with automated intelligence. This achieves high extent of automation while managing complexity through specialized neural network architecture designed for drone navigation.
Solution Approach 2:
The neural network acts as an intermediary between sensor inputs and flight control outputs, translating raw sensor data into autonomous navigation decisions. This intermediary layer enables autonomous operation by mediating between the physical drone systems and the intelligence required for adaptive control, achieving automation without requiring complex direct integration of all subsystems.
3Measurement precision
If drones closely approach assets for inspection, then inspection quality improves, but safety risks increase from obstacles and weather conditions
Solution Approach 1:
The patent implements feedback through continuous monitoring of sensor data during flight, with the neural network constantly analyzing obstacle detections and weather conditions. When potential hazards are detected, the system provides feedback to adjust flight path and approach patterns in real-time, enabling the drone to maintain close inspection distances while dynamically avoiding hazards. This feedback loop resolves the contradiction by enabling precision inspection with safety margins.
Solution Approach 2:
The drone employs dynamic flight path adjustment based on real-time neural network analysis of environmental conditions. The approach patterns are not fixed but dynamically modified according to detected obstacles and weather, allowing the drone to optimize inspection quality while adapting to changing safety conditions. This dynamic capability enables close inspection when safe and maintains distance when hazards are present.
4Productivity
If drones execute preprogrammed flight paths without adaptation, then mission execution efficiency is maintained, but GPS drift and course deviations cause the drone to go off-course
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network with GPS drift compensation algorithms before mission execution. The neural network is trained to recognize and correct for typical GPS drift patterns, enabling the drone to maintain accurate waypoint targeting despite GPS inaccuracies. This resolves the contradiction by preparing correction capabilities in advance, maintaining both efficiency and precision during autonomous operation.
Solution Approach 2:
The drone performs self-correction of GPS drift through autonomous neural network processing of position data. The system continuously monitors its own navigation accuracy and automatically adjusts flight path calculations to compensate for drift, maintaining waypoint precision without requiring external correction inputs. This self-service navigation correction maintains both mission efficiency and positioning accuracy.
Data Source
AI summary
An unmanned aerial vehicle (UAV) or “drone” executes a neural network to assist with inspection, surveillance, reporting, and other missions. The drone inspection neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation to an asset along a preprogrammed flight path and/or during its mission (e.g., as it scans and inspects an asset).