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Optimal Maneuvering for Autonomous Vehicle Self-Localization.

John L McGuire¹, Yee Wei Law¹, Kutluyıl Doğançay¹

¹UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.

Entropy (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

This study introduces an informative path planning algorithm for autonomous vehicles in GPS-denied environments. The algorithm optimizes maneuvering to minimize estimation errors using angle-of-arrival measurements.

Keywords:

angle-of-arrival localization autonomous systems information and sensor fusion informative path planning optimal maneuvering optimization and planning

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Area of Science:

Robotics and Autonomous Systems
Navigation and Control
Signal Processing

Background:

Autonomous vehicles require robust self-localization in Global Navigation Satellite System (GNSS)-denied environments.
Angle-of-arrival (AOA) measurements from known beacon locations offer an alternative for self-localization.
Minimizing position and heading estimation error is critical for mission success.

Purpose of the Study:

To develop an informative path planning (IPP) algorithm for autonomous vehicles.
To minimize the self-localization estimation error covariance determinant using an unscented Kalman filter.
To optimize vehicle heading for improved navigation accuracy.

Main Methods:

Utilized the determinant of the unscented Kalman filter's estimation error covariance matrix as the objective function.
Implemented an l-step look-ahead (LSLA) algorithm for optimal heading determination.
Incorporated vehicle kinematic constraints and AOA measurement characteristics into the planning algorithm.

Main Results:

The proposed IPP algorithm effectively guides the vehicle to maneuver for minimum estimation uncertainty.
Achieved estimation error close to the theoretical lower bound across various scenarios.
Demonstrated successful navigation even with suboptimal beacon placement.

Conclusions:

The informative path planning algorithm enhances autonomous vehicle navigation in GNSS-denied conditions.
Optimal maneuvering based on AOA measurements significantly reduces localization errors.
The algorithm provides a robust solution for self-localization challenges in autonomous systems.