Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques

Rashid, Razif; Elamvazuthi, Irraivan; Begam, M. Rigana; Arrofiq, Muhammad

doi:10.1109/ams.2010.23

Cited by 21 publications

(13 citation statements)

References 5 publications

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“…Because of using many sensors to sense the environment, we do not have any idea on the real time operation. Another fuzzy logic for indoor navigation has been presented in [13]. The authors proposed how to use fuzzy logic control for target tracking control of Wheeled Mobile Robot (WMR).…”

Section: B Mobile Robot Navigationmentioning

confidence: 99%

Smart mobile robot for security of low visibility environment

Faisal

Mathkour

Alsulaiman

2015

2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW)

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The field of robotics has apparently appeared to replace jobs that humans usually dislike to do, jobs that have low tolerance for faults and risks or jobs for which human resource is not available. For these reasons, many types of robot have appeared, such as man-like robots, car-like robots and mobile robots. Mobile robots have become a target for many applications, such as security patrolling , manufacturing, materials handling in the warehouse, and many others applications. In the last decade, mobile robots have become an important topic in the security field. Several methods have been introduced to deal with mobile robot and security. Nevertheless, most of these methods are not smart enough to work in low visibility environments. In this paper, we are going to introduce a novel smart security system, which uses a mobile robot as a security patrol in dark environment. The proposed system is a non-vision based system and designed to deal with the dark environments using artificial intelligence methods (Neural Network NN and Fuzzy Logic FL). The proposed system includes a motion control model, which is responsible for the navigation operation in the monitoring area. The NN and FL techniques are used to smartly determine the abnormal situation in the monitoring area. Many levels of alarms are used to tackle the deferent level of dangers.

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Section: B Mobile Robot Navigationmentioning

confidence: 99%

Smart mobile robot for security of low visibility environment

Faisal

Mathkour

Alsulaiman

2015

2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW)

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“…Another method was introduced for an indoor navigation technique using a fuzzy logic control (Rashid et al, 2010). The method proposed the use of the fuzzy logic control (FLC) in the target tracking control for wheeled mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

Al-Mayyahi

Wang

Hussein

et al. 2017

IJICC

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Purpose -The motion control of unmanned ground vehicles is a challenge in the industry of automation. In this paper, a fuzzy inference system based on sensory information is proposed for the purpose of solving the navigation challenge of unmanned ground vehicles in cluttered and dynamic environments.Design/methodology/approach -The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the fuzzy inference system which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings -Experimental analyses conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the fuzzy inference system. The reported simulation results were obtained using MATLAB software package. The results show that the controllers of the fuzzy inference system consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications -The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move to at various speeds and directions. The methodology of designing the fuzzy inference system is accomplished to guide the unmanned ground vehicle to the desired destination while avoiding collisions with obstacles.However, our methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value -This paper presents the design of a fuzzy inference system and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. That facilitates an improved functionality of the operation of unmanned ground vehicles.The first author would like to express his gratefulness for his sponsor, the Ministry of Higher Education and Scientific Research in Iraq for funding his PhD scholarship in the United Kingdom. He also would like to thank the University of Basrah in Iraq for the support.2

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“…By taking into account adaptive to home environment, there are several different fuzzy controllers are proposed to achieve the autonomous navigation in unknown environment [2,3].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy logic navigated service mobile robot

Lee

Chiu

2013

2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY)

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Over past few decades, the society has seen the stunning transformation of low birth rate and dual-earner households. As a result more and more robots are design for helping people at home. However, most of them are design for specific tasks. This paper proposed an intelligence service robot aim to cope with wide range application in home. To let the robot move freely in home environment and reach one desired target position, two fuzzy logic control systems are proposed as autonomous navigation and obstacle avoidance. The distance information is proved by sonar sensor as the input of our fuzzy systems and the local navigation uses fuzzy logic to control the robot's behavior. The results show the intelligence service robot is adaptive to different environment. The experimental results shows the system can effectively travelled under the effect of time delays over the network and sensor uncertainties

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Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques

Cited by 21 publications

References 5 publications

Smart mobile robot for security of low visibility environment

Smart mobile robot for security of low visibility environment

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

A fuzzy logic navigated service mobile robot

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