https://github.com/francomano/ukf_test

In this project we proposed a tracking control strategy for a tracked mobile robot under longitudinal slip condition. The proposed control strategy is based on the dynamic model of the tracked robot, in which the longitudinal slip of the left and right tracks are described by two unknown parameters.
https://github.com/francomano/ukf_test

autonomous estimation mobile-robots pal-robotics python3 robotics tiago-robot ukf-localization wmr

Last synced: about 1 month ago
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In this project we proposed a tracking control strategy for a tracked mobile robot under longitudinal slip condition. The proposed control strategy is based on the dynamic model of the tracked robot, in which the longitudinal slip of the left and right tracks are described by two unknown parameters.

• Host: GitHub
• URL: https://github.com/francomano/ukf_test
• Owner: francomano
• Created: 2024-02-26T23:48:25.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-18T15:57:52.000Z (almost 2 years ago)
• Last Synced: 2025-01-01T02:26:40.299Z (over 1 year ago)
• Topics: autonomous, estimation, mobile-robots, pal-robotics, python3, robotics, tiago-robot, ukf-localization, wmr
• Language: Jupyter Notebook
• Homepage:
• Size: 13.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
A nonlinear feedback control law is proposed to achieve the trajectory-tracking objective, using estimation of the slip parameters. 

The unscented Kalman filter (UKF) is introduced to joint estimate the states and the slip parameters.

N.B. The UKF algorithm is built in the *complete_controller.py* file.