Slippage Estimation of Two Wheeled Mobile Robot Using Recurrent Deep Neural Network

Date

2019-12-14

Author

Özçil, Osmail
Koku, Ahmet Buğra
Konukseven, Erhan İlhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

209
views

0
downloads

Position, velocity and acceleration information are important for mobile robots. Due to the wheel slippages, encoder data may not be reliable and IMU data also contains a cumulative error. Errors of inertial measurements are accumulated over velocity and position estimates and as time increases, these errors grow higher. Due to robot hardware and the operating surface, ground truth may not be available. In this work recurrent deep neural network is proposed in order to reduce the error in speed and yaw angle estimates coming from encoder and IMU data. Neural networks are commonly used to capture the behavior of linear and nonlinear systems. Since ground-wheel interaction forces are modeled with non-linear models such as the Magic formula and determining parameters of those models require time and test setups, there is a need for simpler methods to model the behavior of simple robots. Neural networks could be used to model non-linear systems. In this work, a recurrent deep neural network is proposed to estimate the speed and yaw angle of a two-wheeled differentially driven mobile robot. Using the information coming from the camera positioned above the test area as ground truth, the network is trained. After that, the output of the network is recorded in the absence of ground truth information in the network. Finally, the performance of the network is evaluated using network output, sensor data calculation, and ground truth.

Subject Keywords

Computing methodologies, Machine learning

URI

https://hdl.handle.net/11511/46088

DOI

https://doi.org/10.1145/3388218.3388233

Collections

Department of Mechanical Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Cogging Torque Disturbance Rejection for a Low-cost Gimbal Motor and a Controller Design with Practical Considerations Ozdogan, Gokhan; Leblebicioğlu, Mehmet Kemal (2019-01-01) In many robotic applications and inertially stabilized electro-optic gimbal systems, precise positioning and speed control are highly important concepts. Due to size and weight limitations, motor is required to be small but torque density is desired to be high. In high torque and cheap PMSM and BLDC motors, cogging torque and friction are usually the challenging disturbance sources. In this study, cogging torque and friction are identified using position sensors which already exist in gimbal systems, so the...
Slippage estimation of a two wheeled mobile robot using deep neural network Özçil, İsmail; Konukseven, Erhan İlhan; Koku, Ahmet Buğra; Department of Mechanical Engineering (2018) Mobile robot navigaiton is an important task for the operations of the mobile robots. Due to the wheel slippages, performance of the dead reckoning in estimating speed of the robot and the position of the robot is not sufficient. To overcome the errors in navigation estimates, usage of the recurrent deep neural networks is porposed. Neural networks are used to understand the behaviour of the linear and nonlinear systems. Since wheel-ground interaction will be modeled with non-linear models and the estimatin...
Feature Detection Performance Based Benchmarking of Motion Deblurring Methods: Applications to Vision for Legged Robots Gultekin, Gokhan Koray; Saranlı, Afşar (Elsevier BV, 2019-02-01) Dexterous legged robots can move on variable terrain at high speeds. The locomotion of these legged platforms on such terrain causes severe oscillations of the robot body depending on the surface and locomotion speed. Camera sensors mounted on such platforms experience the same disturbances, hence resulting in motion blur. This is a particular corruption of the image and results in information loss further resulting in degradation or loss of important image features. Although motion blur is a significant pr...
Electromagnetic interaction complexity reduction using deep learnin Karaosmanoğlu, Barışcan; Ergül, Özgür Salih; Department of Electrical and Electronics Engineering (2019) In this thesis, we present a novel approach to accelerate electromagnetic simulations by the multilevel fast multipole algorithm (MLFMA). The strategy is based on a progressive elimination of electromagnetic interactions, resulting in trimmed tree structures, during iterative solutions. To systematically perform such eliminations, artificial neural network (ANN) models are constructed and trained to estimate errors in updated surface current coefficients. These column eliminations are supported by straightf...
Dynamic modeling and parameter estimation for traction, rolling, and lateral wheel forces to enhance mobile robot trajectory tracking BAYAR, Gokhan; Koku, Ahmet Buğra; Konukseven, Erhan İlhan (Cambridge University Press (CUP), 2015-12-01) Studying wheel and ground interaction during motion has the potential to increase the performance of localization, navigation, and trajectory tracking control of a mobile robot. In this paper, a differential mobile robot is modeled in a way that (traction, rolling, and lateral) wheel forces are included in the overall system dynamics. Lateral wheel forces are included in the mathematical model together with traction and rolling forces. A least square parameter estimation process is proposed to estimate the ...

Citation Formats

O. Özçil, A. B. Koku, and E. İ. Konukseven, “Slippage Estimation of Two Wheeled Mobile Robot Using Recurrent Deep Neural Network,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46088.