Abstract
This paper presents a newly developed approach for Differential Drive Mobile Robot (DDMR). The main goal is to provide a high dynamic system response in the joint space level, the low level control, as well as to enhance the DDMR localization. The proposed approach depends on a Linear Quadratic Regulator (LQR) for the low level control and an Adaptive LQR for the high level control. The investigated DDMR is considered highly nonlinear system due to uncertainty exhibited by the mobile robot incorporated with actuators nonlinearity. DDMR’s uncertainty leads to erroneous localization. An Extended Kalman Filter (EKF) -based approach with fusion sensors is used to enhance the robot degree of belief for its posture. Intensive simulation results obtained from the developed uncertain model and the proposed approach have shown very good dynamic performance on the low level control and very good convergence to the desired posture of the mobile robot path with the presence of robot uncertainty.
Highlights
The question of “where am I?” exhibited by mobile robots, in general, remains challenging and incompletely covered in academics
[3, 4] use Extended Kalman Filter (EKF) to purify the information about the robot‟s localization. [6, 8] have proposed trajectory tracking algorithm but considered the information comes from the proprioceptive sensors is correct enough to determine the robot‟s posture with no lack of accuracy
This paper focuses on a novel control approach utilizing Linear Quadratic Regulator for joint space control of robot actuators as well as proposing EKF-assisted optimal controller to overcome the problem of robot uncertainty, which may lead to robot posture divergence
Summary
The question of “where am I?” exhibited by mobile robots, in general, remains challenging and incompletely covered in academics. The onboard sensors (proprioceptive sensors) assumed noisy, in addition to the robot‟s mechanical parameters considered highly uncertain It is presumed, as well, that the mobile robot is due to some random disturbance represented by τd which is very common in the system control areas [3]. The remaining sections of the paper are as follows: Section II provides details about the dynamic model of the mobile robot incorporating the actuators dynamics; section III explains the design of the proposed controller for the joint space control system i.e., low level control; section IV discusses mobile robot navigation and localization; section V exhibits intensive simulation results with different system uncertainty and section VI provides a conclusion for the presented work
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