Fuzzy logic controllers design for omnidirectional mobile robot navigation

Highlights

• Most of the previous works are focused on only the estimation of the final navigation position of the mobile robot.

• The omnidirectional robot has to reach the final desired position with a predefined final angle.

• The optimization of the travelled distance using an appropriate FLC.

• Simulation and experimental tests are performed for one, two and three obstacles to evaluate the real performances of the developed algorithms.

Abstract

This paper presents a new design approach for an intelligent navigation algorithm for omnidirectional mobile robots. Unlike the previous works dealing with the navigation of omnidirectional robots that are focused on only the estimation of the final position, the main contribution of the present study is summed up in the fact that the robot has to reach the final desired position with a predefined final steering angle. This latter improvement is a part of researches carried out on Intelligent Transport Systems (ITS). Taking into consideration the drawbacks of proportional integral (PI) control when applied to omnidirectional robot navigation, we develop an approach to design a fuzzy logic PI controller (Fuzzy-PI).

Preliminary simulation and experimental results using the Fuzzy-PI controller have shown limitations as the robot performed a larger path when the desired final angle increased. Thus, a deepen study have concluded that the Fuzzy-PI system cannot control at the same time the linear and angular velocities. To overcome these drawbacks, we propose to replace the previous intelligent navigation system by two independent controllers. The designed Fuzzy-PI controllers can adjust their parameters (K_P, K_I) to reduce the error caused by the dynamic changes and navigation challenges of omnidirectional robot. A navigation algorithm cannot be efficient without obstacle avoidance system. To achieve this goal, we have developed a third fuzzy controller to fulfill this task.

To evaluate the real performances of fuzzy controllers for navigation and obstacles avoidance, simulation and experimental tests are performed for one, two and three obstacles. The obtained results confirm that the omnidirectional robot could navigate in an unstructured and unknown obstacles environment with better performances and efficiency.

Introduction

Fuzzy logic theory is a powerful soft computing technique to control complex and non-linear systems based on human expert knowledge. In this paper, the navigation and obstacles avoidance problems of a Robotino omnidirectionnel robot is addressed using different fuzzy-logic control algorithms. Currently, omnidirectional vehicles are becoming increasingly popular in both academic and industrial fields, e.g. mobile robots. The latter have some distinguishing advantages over nonholonomic mobile robots. With its three degrees of freedom, omnidirectional mobile mechanism has the great ability to move at each instant in any direction without reorientation. The navigation control of the mobile robot can be classified in two main types: trajectory or path planning’s. The first one generates a path between two points with eventually a collision avoiding system. The second type tries to follow the path generated from à predefined lane with a minimal error. However, the navigation of omnidirectionnel robots is a very challenging problem. In fact, classical approaches do not work appropriately since they need the analytic model of the system. However, this modeling is not always available and sometimes impossible to formulate. One of the classical controllers is the famous PID algorithms used for robot control. However, the major drawback of this conventional controller is the need of its parameters estimation based on system model. These parameters rely on the system identification conditions such as the operating point, or if the plant parameters changed. In this case, PID parameters cannot compensate the alterations adaptively [1]. In order to overcome these problems, soft-computing systems are considered as a powerful tool to control a mobile robot [1], [2], [3], [4], [5], [6], [7]. Fuzzy-logic is considered as a good technique to solve problems, dealing with imprecise aspect and when human expert based-knowledge is provided, making it an adequate theory to develop an omnidirectionnal robot navigation algorithm. Previous academic works have used the fuzzy-logic theory to design a Robotino navigation control algorithm [8], [9]. Robot navigation performances can be improved by developing a fuzzy adaptive PI-controller that combines the advances of conventional PI with the fuzzy logic theory [10], [11], [12].

Based on these previous literature works, one could state that PID controller combined with fuzzy-logic is considered to be an effective soft computing technique to control omnidirectionnal robot navigation. Fuzzy-PI controller is a powerful algorithm that can be used when the system is difficult to model, and since the conventional control technique does not provide the expected results even when expert knowledge’s are available.

The main requirements for Robotino navigation, addressed in this work, are the following: (i) reach the target position with a desired final angle, (ii) minimize the traveled distance and (iii) avoid occurred obstacles. To fulfill these requirements, a Fuzzy-PI controller was firstly designed and tested. However, the obtained navigation performances confirmed that the same fuzzy-controller could not be used for controlling together the linear and angular velocities. To overcome these drawbacks, we propose to replace the previous intelligent navigation system by two independent Fuzzy-PI controllers. Moreover, to achieve the navigation requirements, we propose to develop an obstacle avoidance system using a third fuzzy logic controller.

The paper is organized as follows. Section 2 introduces the kinematic modeling of the omnidirectional robot. Section 3 details Fuzzy adaptive PI controller architecture and simulation results obtained using Matlab and Robotino-Sim environments. Section 4 presents the Fuzzy-PI drawbacks and the developped solution to improve the navigation performances and to optimize the Fuzzy-PI controller. An obstacle avoidance system based on fuzzy logic is detailed in Section 5. Section 6 presents the real-time implementation of intelligent navigation system on Robotino PC-104 processor. In this section, experimental results are also detailed and performances of mobile robot navigation are evaluated. Finally, Section 7 draws the conclusion.