Drone Dead Reckoning Correction Using Projected Light Patterns
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Solution Overview
Problem
Drones using dead reckoning navigation methods experience accuracy degradation over time due to errors in accelerometer measurements, leading to untenable navigation.
Innovation Solution
Implementing a system that uses projected light patterns and sensor synthesis with accelerometers, gyroscopes, and magnetometers to correct for drift, allowing drones to determine their absolute angle and position with high precision by following and aligning with projected patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dead reckoning navigation is used with accelerometers, then the drone can determine position without external references, but measurement precision deteriorates over time due to error accumulation
Solution Approach 1:
The system projects light patterns onto the environment and uses onboard cameras to detect these patterns, creating a feedback loop that continuously corrects position and orientation estimates. The detected pattern positions are compared with expected positions to generate correction signals that compensate for drift accumulation in the dead reckoning system.
Solution Approach 2:
Light patterns serve as an intermediary reference medium between the drone and the environment. These projected patterns create artificial visual landmarks that the drone can detect and use for position verification, eliminating the need for natural landmarks or GPS while providing continuous absolute position references.
2Measurement precision
If multiple sensors (accelerometers, gyroscopes, magnetometers) are combined for sensor synthesis, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (accelerometers, gyroscopes, magnetometers) into a unified sensor synthesis framework. By fusing these diverse sensor inputs through algorithms that account for each sensor's characteristics and error modes, the system achieves higher measurement precision while managing complexity through integrated processing.
Solution Approach 2:
The system replaces complex mechanical reference systems with sensor-based detection and computation. Instead of using physical reference markers or external infrastructure, the drone uses sensor synthesis to computationally determine position and orientation, substituting mechanical complexity with electronic sensing and algorithmic processing.
3Reliability
If light patterns are projected for orientation correction, then navigation reliability improves, but use of energy increases
Solution Approach 1:
The light projection system operates periodically rather than continuously, projecting patterns at intervals sufficient to correct drift accumulation while allowing energy savings during non-projection periods. This periodic operation maintains navigation reliability by providing correction references at critical moments without sustaining continuous energy consumption.
Solution Approach 2:
The system maintains continuous navigation capability through sensor synthesis while using periodic light projection only when drift correction is needed. The sensor fusion algorithms continuously process data to maintain position estimates, and the light projection is activated only when correction is required, ensuring continuous useful action with minimized energy expenditure.
Data Source
AI summary
Dead reckoning correction utilizing patterned light projection is provided herein. An example method can include navigating a drone along a pattern using dead reckoning, the pattern having a plurality of lines, detecting one of the plurality of lines using an optical sensor of the drone, determining when a line of travel of the drone is not aligned with the one of the plurality of lines, and realigning the line of travel of the drone so as to be aligned with the one of the plurality of lines to compensate for drift that occurs during navigation using dead reckoning.