UAV Touchdown Detection Using Force Estimation During Landing
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Solution Overview
Problem
Autonomous UAVs face challenges in safely and autonomously determining touchdown on various surfaces without tactile force sensors, particularly when landing on dynamic or uneven surfaces, and require a reliable method to power down propulsion systems without human intervention.
Innovation Solution
The technique integrates sensor information with a dynamics model to estimate external forces and torques acting on the UAV, enabling it to detect touchdown and adjust propulsion accordingly, using a combination of rules and machine learning to ensure safe landing on diverse surfaces, including being grabbed by a person.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tactile force sensors are used to detect touchdown, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces tactile force sensors (mechanical contact-based detection) with an optical imaging system that uses computer vision to detect touchdown. The imaging device captures images of the landing surface, and touchdown is detected by analyzing changes in the visual field, feature point movements, or image content between frames. This substitution eliminates the need for complex mechanical force sensors while maintaining touchdown detection capability.
Solution Approach 2:
The patent introduces an intermediary processing system that analyzes visual data from the imaging device to infer touchdown conditions. Instead of directly measuring force, the system uses image processing algorithms to detect changes in the visual environment that indicate touchdown, such as sudden appearance of ground features, changes in perspective, or stabilization of the visual field. This intermediary approach enables indirect detection without requiring direct mechanical contact sensors.
2Adaptability or versatility
If the UAV lands on dynamic or uneven surfaces, then adaptability is improved, but measurement precision of touchdown detection deteriorates
Solution Approach 1:
The patent employs dynamic image processing that adapts to changing landing conditions. The system continuously captures images during descent and uses real-time analysis to detect touchdown regardless of surface characteristics. The processing algorithm dynamically adjusts to account for surface motion, unevenness, or deformation by tracking feature points across multiple frames and analyzing their movement patterns, enabling accurate touchdown detection on diverse surfaces including dynamic ones.
Solution Approach 2:
The system uses feedback from continuous image capture and analysis to refine touchdown detection. By monitoring changes in the visual field across multiple frames during the landing sequence, the system can detect the moment of touchdown through sudden changes in image content, feature point stability, or perspective shifts. This feedback mechanism allows the system to adapt to various surface conditions and accurately identify touchdown even when the surface is dynamic or uneven.
3Extent of automation
If autonomous touchdown detection is implemented, then extent of automation is improved, but reliability of safe landing deteriorates without tactile sensors
Solution Approach 1:
The imaging device serves multiple functions: it captures navigation information during flight, provides obstacle detection, and enables touchdown detection. By using a single multi-functional sensor system rather than separate specialized sensors, the patent achieves autonomous landing capability while maintaining reliability through redundant use of the same hardware for multiple critical functions. The imaging system's versatility allows it to provide sufficient information for safe touchdown detection without requiring additional tactile sensors.
Data Source
AI summary
A technique is introduced for touchdown detection during autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing perception inputs with a dynamics model of the aerial vehicle to estimate the external forces and/or torques acting on the aerial vehicle. The estimated external forces and/or torques are continually monitored while the aerial vehicle is landing to determine when the aerial vehicle is sufficiently supported by a landing surface. In some embodiments, semantic information associated with objects in the environment is utilized to configure parameters associated with the touchdown detection process.