Vehicle Navigation Using Ackermann Geometry for Sensor Position
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Solution Overview
Problem
Integrated MEMS IMU/GPS navigation systems face significant positioning errors when GPS signals are lost due to urban canyons or buildings, as existing methods fail to accurately account for non-zero side velocities, leading to divergent navigation solutions and large positioning errors.
Innovation Solution
The proposed navigation system utilizes the internal geometry of the sensor position with respect to the vehicle's rear-wheel axis, specifically incorporating Ackermann Steering Geometry, to enhance navigation accuracy by automatically estimating the distance between the sensor and the rear wheel axis using a Kalman filter, thereby minimizing positioning errors even without GPS signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If conventional Kalman filter methods assume zero side velocity for ground vehicles, then navigation solution stability is improved, but positioning accuracy deteriorates due to erroneous suppression of non-zero analytical velocity terms
Solution Approach 1:
The patent changes the velocity constraint parameter from zero assumption to a calculated non-zero value based on sensor position geometry. By deriving side velocity as a function of longitudinal velocity and angular rate (v_by = -d * ω_bz), the system maintains navigation stability while achieving accurate positioning, resolving the contradiction between stability and precision.
Solution Approach 2:
The patent introduces an intermediary relationship through Ackermann steering geometry that connects the sensor position distance (d) with vehicle dynamics. This intermediary model allows the system to calculate the actual non-zero side velocity analytically, bridging the gap between stability requirements and accuracy needs without directly measuring side velocity.
2Ease of manufacture
If low-cost MEMS sensors are used in IMU, then device cost is reduced, but positioning accuracy deteriorates due to large bias and noise in MEMS sensors
Solution Approach 1:
The patent implements feedback through the Kalman filter that continuously estimates and corrects MEMS sensor biases and noise. By using the analytical side velocity relationship as a feedback constraint, the system compensates for MEMS errors, maintaining high positioning accuracy despite using low-cost sensors.
Solution Approach 2:
The system performs self-calibration by automatically estimating the sensor position distance (d) and using it to calculate the analytical side velocity. This self-service mechanism allows the low-cost MEMS system to correct its own errors without external reference, achieving high accuracy despite sensor limitations.
3Reliability
If GPS signals are lost in urban canyons or buildings, then navigation independence is improved, but positioning accuracy deteriorates due to error accumulation in INS
Solution Approach 1:
The patent prepares for GPS loss by pre-establishing the analytical side velocity relationship based on vehicle geometry and dynamics. When GPS signals are lost, this pre-established relationship immediately becomes available to constrain the INS solution, preventing error accumulation and maintaining positioning accuracy during GPS outages.
Solution Approach 2:
The system cushions against GPS signal loss by incorporating the analytical side velocity constraint into the Kalman filter before GPS dropout occurs. This beforehand cushioning ensures that when GPS signals are lost in urban canyons or buildings, the INS/GPS integrated system can maintain accuracy without divergent error accumulation.
Data Source
AI summary
A navigation system and method to utilize the internal geometry of the sensor position with respect to the vehicle's rear-wheel axis for maintaining high positioning accuracy even when GPS signals are lost for a long period of time are disclosed. One aspect is to use an analytical condition derived from a vehicle's mechanical condition so-called Ackermann Steering Geometry for enhancement in navigation accuracy. The analytical condition is a relationship between the vehicle's lateral directional velocity, the distance of the sensor position with respect to the rear wheel axis, and the angular rate with respect to the vehicle's z-axis. Another aspect is to incorporate the distance of the sensor position with respect to the rear wheel axis into the INS and Kalman filter's states as an auxiliary parameter.