Autonomous navigation of mobile robot with combined fractional order PI and fuzzy logic controllers

Allagui, Najah Yousfi; Abid, Donia Benhalima; Derbel, Nabil

doi:10.1109/ssd.2019.8893176

Cited by 8 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their method highlights the importance of sensor integration for effective obstacle detection. In a study by Allagui et al [30], the authors investigate using a combination of fractional order PI and fuzzy logic controllers to enable autonomous navigation. Their research highlights the advantages of employing hybrid control strategies for mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Khan,

Kunwar,

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The primary objective of this study is to investigate the effects of mobile robot navigation using a fuzzy logic framework based on Z-numbers implemented within the Robot Operating System (ROS) Noetic. The methodology addresses uncertainty and imprecise information in robot navigation using extensive simulations performed using the TurtleBot3 robot in the ROS framework. Our unique approach enables the autonomous navigation of mobile robots in unknown environments, utilizing fuzzy rules with multiple inputs and outputs. The navigation strategy relies on the laser scan sensor, the Adaptive Monte Carlo Localization (AMCL) algorithm, and particle filter mapping, enabling real-time localization and mapping capabilities. Path planning incorporates local and global planners, while obstacle avoidance generates collision-free paths by dynamically detecting and circumventing obstacles in the robot's proximity. We employ Simultaneous Localization and Mapping (SLAM) techniques to estimate the robot's position and create a map of the environment. Our integration of these methods presents a promising solution for autonomous mobile robot navigation in real-world applications, thereby advancing the capabilities of robot systems in complex environments. Our results demonstrate the suitability and effectiveness of using a Z-number-based system in the navigation scenarios of mobile robots.

show abstract

Section: Introductionmentioning

confidence: 99%

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Khan,

Kunwar,

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Initially, the object is made stable relative to a certain point in the state space. Then, the stability points are positioned along the desired path and the object is moved along the trajectory by following these points from one point to another [1][2][3][4][5][6][7]. The difference between the existing methods is in solving the control synthesis problem to ensure stability relatively to some equilibrium point in the state space and in the location of these stability points.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Synthesized Control for Solving the Optimal Control Problem

Diveev,

Shmalko

2023

Mathematics

View full text Add to dashboard Cite

The development of artificial intelligence systems assumes that a machine can independently generate an algorithm of actions or a control system to solve the tasks. To do this, the machine must have a formal description of the problem and possess computational methods for solving it. This article deals with the problem of optimal control, which is the main task in the development of control systems, insofar as all systems being developed must be optimal from the point of view of a certain criterion. However, there are certain difficulties in implementing the resulting optimal control modes. This paper considers an extended formulation of the optimal control problem, which implies the creation of such systems that would have the necessary properties for its practical implementation. To solve it, an adaptive synthesized optimal control approach based on the use of numerical methods of machine learning is proposed. Such control moves the control object, optimally changing the position of the stable equilibrium point in the presence of some initial position uncertainty. As a result, from all possible synthesized controls, one is chosen that is less sensitive to changes in the initial state. As an example, the optimal control problem of a quadcopter with complex phase constraints is considered. To solve this problem, according to the proposed approach, the control synthesis problem is firstly solved to obtain a stable equilibrium point in the state space using a machine learning method of symbolic regression. After that, optimal positions of the stable equilibrium point are searched using a particle swarm optimization algorithm using the source functional from the initial optimal control problem statement. It is shown that such an approach allows for generating the control system automatically by computer, basing this on the formal statement of the problem and then directly implementing it onboard as far as the stabilization system has already been introduced.

show abstract

“…A novel method for the arithmetic mean-based decision-making system is described in [6]. Recently, there is a growing interest in fuzzy-controlled mobile robots [7][8][9]. Dynamic-Window Method (DWM) [10] converts the environmental data in the classical Cartesian coordinate system into linear and angular velocities as references, computes the weighted sum of distances, and avoids the placed obstacles by selecting the objective function's largest value.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mobile Robot With Obstacle Avoidance Using Fuzzy Controller

Khudaverdiyeva

2022

A.I.S.

View full text Add to dashboard Cite

This paper presents the modeling of a robot's navigation using ultrasonic sensors under uncertainty. The robot tries to avoid obstacles by using the fuzzy logic controller to process the data coming from three ultrasonic sensors. To assess the performance of fuzzy logic optimized robot navigation controller with ultrasonic sensors, which measure the distance by calculating the time spent on the object and its return, the obstacles are placed in front of, left, and right of the robot. Mamdani fuzzy reasoning system is used for the designed controller for its intuitive properties and fewer setting parameters which reduces the amount of time spent on the programming of the controller. 25 rules are considered to cover a robot’s possible interactions with obstacles. For an easy understanding of navigation architecture and rapid algorithm implementation, in this paper, a MATLAB simulation framework is developed. MATLAB/Simulink is one of the best simulation tools required to design the architecture and verify algorithms with real-time constraints. Resultant models of the fuzzy optimized controller demonstrate the superior performance of the fuzzy logic controller with high adaptability to the environment while maintaining a sufficient level of accuracy. The designed fuzzy controller can be used in microprocessor/microcontroller-based robots owing to easiness in implementation and coding.

show abstract

Autonomous navigation of mobile robot with combined fractional order PI and fuzzy logic controllers

Cited by 8 publications

References 7 publications

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Adaptive Synthesized Control for Solving the Optimal Control Problem

Modeling of Mobile Robot With Obstacle Avoidance Using Fuzzy Controller

Contact Info

Product

Resources

About