Autonomous Control of a Line Follower Robot Using a Q-Learning Controller

01/23/2020
by   Sepehr Saadatmand, et al.
2

In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfaces, system modeling and controller designing can be extremely challenging. The mathematical modeling for the robot is presented in this paper, and a simulator is designed based on this model. The basic Q learning methods are based pure exploitation and the epsilon-greedy methods, which help exploration, can harm the controller performance after learning completion by exploring nonoptimal actions. The simulated annealing based Q learning method tackles this drawback by decreasing the exploration rate when the learning increases. The simulation and experimental results are provided to evaluate the effectiveness of the proposed controller.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

page 6

04/14/2021

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

As a model is only an abstraction of the real system, unmodeled dynamics...
12/29/2020

An LQR-assisted Control Algorithm for an Under-actuated In-pipe Robot in Water Distribution Systems

To address the operational challenges of in-pipe robots in large pipes o...
06/07/2021

Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control

Legged robots can traverse challenging terrain, use perception to plan t...
08/06/2019

Design, Modeling, and Control of Norma: a Slider & Pendulum-Driven Spherical Robot

This paper discusses the design, modeling, and control of Norma, a novel...
03/03/2020

Modeling and Control of a Hybrid Wheeled Jumping Robot

In this paper, we study a wheeled robot with a prismatic extension joint...
09/13/2021

A Q-learning Control Method for a Soft Robotic Arm Utilizing Training Data from a Rough Simulator

It is challenging to control a soft robot, where reinforcement learning ...
04/02/2020

Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment

Exploring and traversing extreme terrain with surface robots is difficul...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.