Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real  Swarm Robot

Processing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python.

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“…In the paper [46], An experiment with a swarm robot shows that type-2 fuzzy logic has better performance than type-1 fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Queries allocation in WSNs with fuzzy control system

Abbassi

Ezzedine

2023

IET Communications

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“…Applications of the fuzzy system are useful in every field of life like Cognitive Science, Robotics, Industry, Medical Field, Agriculture, Weather forecasting, etc. Fuzzy based simulation for attention mechanism in CR is performed to describe the level of attention required based on the type of motion and FE [32,33]. In this research, attention depends upon the selected values of FE and motion.…”

Section: Proposed Task Planning In Cloud Roboticsmentioning

confidence: 99%

Intelligent task planner for cloud robotics using level of attention empowered with fuzzy system

et al. 2020

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Cloud robotics is a field of robotics that endeavors to summon cloud advances, for instance, cloud computing, and other internet advances focused on the advantages of merged framework and shared administrations for robotics. In this research, an intelligent task planning mechanism is proposed to attain a certain level of attention for knowledge sharing among cloud-based robots using mamdani fuzzy inference system (MFIS). The proposed task planning mechanism using mamdani fuzzy inference system (TPM-MFIS), can classify the different stages of attention such as very high, high, medium, low, very low and none. In this recommended intelligent system, cloud robotics services are used to maintain the workload of each robot. Whereas, all fuzzification and decision-making policies handled on cloud to avoid the redundancy task planning consumption on individual level. This system has two input variables that are facial expression and motion rate. Input variables detect the output level of attention to be very high, high, medium, low, very low, and none. This paper presents an analysis of the result's accuracy using proposed TPM-MFIS to model the task planning process.

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“…A Type-1 fuzzy logic and interval Type-2 fuzzy logic controllers were designed and implemented for a swarm robot system in Ref. [11]. Each of the mobile robots contained one compass sensor, three distance sensors, and one X-Bee communication module.…”

Section: Introductionmentioning

confidence: 99%

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

2022

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Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.

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Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real Swarm Robot

Cited by 7 publications

References 44 publications

Queries allocation in WSNs with fuzzy control system

Queries allocation in WSNs with fuzzy control system

Intelligent task planner for cloud robotics using level of attention empowered with fuzzy system

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

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