Differential Drive Motion Estimation With Adaptive Command Modeling
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Solution Overview
Problem
Conventional localization methods for differential drive vehicles rely heavily on exteroceptive sensors, which suffer from accuracy degradation and loss of localization in GPS-denied areas, and existing methods using Kalman filters are inadequate for accurate motion parameter estimation without external GPS signals.
Innovation Solution
A processor-implemented method and system that utilize time-synchronized command input signals to estimate motion parameters by training a non-linear model, generating an experimental model, and adaptively updating it using sensor data to reduce inaccuracy, primarily relying on interoceptive sensors like wheel encoders and IMU for accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional localization methods use exteroceptive sensors (GPS, camera, LIDAR) for position estimation, then localization accuracy is improved under normal conditions, but localization accuracy degrades or is lost in GPS-denied areas
Solution Approach 1:
The patent extracts and removes the dependency on exteroceptive sensors (GPS, camera, LIDAR) from the localization system. Instead, it focuses on using only interoceptive sensors (wheel encoders, accelerometers, gyroscopes) that are intrinsic to the vehicle itself, thereby eliminating the reliability issue in GPS-denied areas while maintaining localization capability through self-contained sensing.
Solution Approach 2:
The localization system serves itself by using the vehicle's own motion commands and interoceptive sensor measurements without requiring external infrastructure. The system generates its own localization estimates through adaptive modeling of the relationship between control inputs and motion parameters, making the vehicle self-sufficient for localization in any environment.
2Measurement precision
If Kalman filter based methods use velocity updates from wheel encoders during GPS outages, then localization errors are reduced, but the methods remain inadequate for accurate motion parameter estimation without external GPS signals
Solution Approach 1:
The patent implements adaptive feedback mechanisms where the system continuously refines its experimental model using the relationship between control commands and actual motion measurements from interoceptive sensors. This feedback loop compensates for the lack of external GPS signals by learning and adapting to the vehicle's specific dynamics, thereby maintaining accurate motion parameter estimation without external references.
Solution Approach 2:
The system dynamically changes and adapts the parameters of its experimental model based on observed relationships between control inputs and vehicle responses. By continuously updating the model parameters through adaptive identification, the system maintains accurate motion parameter estimation even without external GPS signals, overcoming the limitations of fixed-parameter Kalman filter approaches.
Data Source
AI summary
Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors. Further, reduced number of sensors leads to reduction in cost.