Payload Attitude Estimation Using Vehicle Sensors and EKF
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Solution Overview
Problem
Traditional systems for estimating payload position and attitude on unmanned vehicles face challenges in dynamic environments due to external forces, leading to inaccuracies and increased complexity and cost, which are not adequately addressed by existing solutions.
Innovation Solution
A system and method using a dynamic model and Extended Kalman Filter (EKF) to synchronize and process sensor data from the unmanned vehicle and payload, correcting raw target data for precise payload position and attitude estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed directly on the payload to estimate position and attitude, then measurement precision is improved, but device complexity and cost increase due to duplicating sensors on both vehicle and payload
Solution Approach 1:
The patent extracts the sensor suite from the payload and relocates it to the unmanned vehicle. The vehicle's sensors (IMU, GNSS, barometer) are used to estimate payload position and attitude through mathematical modeling rather than duplicating sensors on the payload. This reduces device complexity while maintaining measurement precision through the use of the extended Kalman filter and dynamic models.
Solution Approach 2:
The patent introduces an intermediary estimation system that bridges the vehicle's sensor data and the payload's position/attitude. The extended Kalman filter acts as a mediator, processing vehicle sensor data through dynamic models to infer payload state without direct sensor contact on the payload, thus reducing complexity while preserving accuracy.
2Measurement precision
If multiple sensors are integrated on both vehicle and payload to improve measurement precision, then reliability is improved, but weight increases impacting vehicle performance
Solution Approach 1:
The patent removes redundant sensors from the payload by extracting the measurement function and relocating it to the vehicle. Only essential payload sensors remain on the payload, while vehicle-mounted sensors perform dual functions of vehicle navigation and payload monitoring, thereby reducing total system weight while maintaining measurement precision.
3Device complexity
If traditional proxy methods are used where vehicle position represents payload position, then device complexity is reduced, but measurement precision deteriorates in dynamic environments due to external forces
Solution Approach 1:
The patent transitions from static proxy methods to dynamic estimation. The extended Kalman filter continuously updates payload position and attitude estimates based on real-time vehicle sensor data and dynamic models that account for external forces like wind and vibrations. This dynamic approach maintains low device complexity while significantly improving measurement precision in challenging environments.
Solution Approach 2:
The patent implements feedback through the extended Kalman filter, which continuously processes vehicle sensor measurements and refines payload state estimates. The filter uses feedback from acceleration, position, and orientation sensors to compensate for external disturbances, maintaining measurement precision without increasing system complexity.
4Reliability
If high precision payload positioning is implemented to improve measurement precision, then reliability of sensitive payloads is improved, but device complexity increases
Solution Approach 1:
The patent extracts the high-precision positioning function from the payload and implements it at the vehicle level. By using the vehicle's robust sensor suite and centralized processing, the system achieves reliable positioning for sensitive payloads without adding complex positioning hardware to each payload, thereby improving reliability while controlling complexity.
Data Source
AI summary
Systems and methods for estimating the position and attitude of a payload mounted on an unmanned vehicle (UV) and for correcting target data derived from the payload. Synchronized data is processed through an Extended Kalman Filter (EKF) to accurately estimate the payload's position and attitude. The payload position and attitude estimates are updated with new sensor data obtained during UV operation, and these estimates are used to correct raw target data from the payload.