Mamdani based Fuzzy Logic Controller for A Wheeled Mobile Robot with Obstacle Avoidance Capability

Najmurrokhman, Asep; Kusnandar, .; Komarudin, Udin; Sunubroto,; Sadiyoko, Ali; Iskanto, Tisna Yanu

doi:10.1109/morse48060.2019.8998720

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Just like pilots who perform CA maneuvers manually based on their visual perception and flight instrument information about incoming static or dynamic obstacles, the proposed algorithm also takes into account the sensor readings for incoming intruders to modulate the current path and avoid collisions. Previously, fuzzy logic for CA was predominantly researched in surface vehicles [38][39][40][41]. Now, research on the use of fuzzy logic for CA in UAVs is also gaining traction.…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Rauniyar,

Kim

2023

Electronics

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Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space.

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Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Rauniyar,

Kim

2023

Electronics

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“…Ці методи використовуються для налаштування входів і виходів контролера нечіткої логіки з метою покращення навігації мобільного робота. У працях [19][20][21] розглянуто альтернативні підходи до реалізації систем керування на підставі нечіткої логіки.…”

Section: =Bdeg/introductionunclassified

Basic structure of the neurofuzzy control system for a group of mobile robotic platforms

Tsmots,

Opotyak,

Shtohrinets

et al. 2023

UJIT

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It is shown that the following approaches can be used for group management of mobile robotic platforms (MRP): centralized (concentrated), decentralized (distributed) and hybrid. It was determined that an urgent task is the development of a neurofuzzy management system for the MRP group, which must perform the distribution of tasks between the MRPs, the determination of MRP movement routes, joint planning of works and their synchronization. The requirements for the system of neurofuzzy management of the MRP group are formulated, the main of which are the provision of: effective management of the MRP group; minimization of time for tasks; flexibility and adaptability to changing working conditions; reliable and stable operation when implementing various scenarios; expansion of functions and scaling relative to the number of MRPs; accuracy and reliability of traffic management of each MRP; response to changes in working conditions; uninterrupted work of the MRP group; effective use of MRP resources; reduction of dimensions, weight and energy consumption; management in real time; collecting data on the environment and the state of the MRP; wireless communication between MRP; development of software tools taking into account the distributed architecture; implementation of a programming interface with the possibility of developing additional software and integration with other systems; saving data on the status of all MRPs for further analysis and improving the management of the MRP group. The following main stages of the development of the neurofuzzy control system by the MRP group were identified: problem formulation; analysis of system requirements; hardware design; development of a neurofuzzy control algorithm; software development; testing and tuning; implementation and operation. It is suggested that the development of the system of neurofuzzy control of the MRP group be carried out on the basis of an integrated approach, which includes: methods of neurofuzzy control of the MRP group, artificial neural networks and fuzzy logic; navigation methods, methods of pre-processing and image recognition; methods of intellectual processing and evaluation of data from sensors in conditions of interference and incomplete information; modern methods and algorithms of intelligent traffic control of MRP; modern element base (microcontrollers, systems-on-chip, FPGA, etc.); methods and means of automated design of hardware and software of MRP. It is proposed to implement the neurofuzzy control system by the MRP group on the basis of a problem-oriented approach, which involves a combination of universal software and specialized hardware, which ensures high efficiency of equipment use. The method of time allocation of resources of the storage medium of multiport memory has been improved, which, due to the consideration of the speed of the storage medium and external devices, ensures an increase in the number of devices with conflict-free access to the storage medium.

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“…Desde que o Sistema de Inferência Fuzzy Mamdani ou MFIS (do inglês, Membership Fuzzy Inference System) foi proposto, diversos problemas de difícil análise e formulac ¸ão matemática têm sido solucionados por meio de uma descric ¸ão linguística implementada na forma de regras baseadas no conhecimento do especialista, como na teoria de sistemas de controle [10], em identificac ¸ão de sistemas [11], em robótica [12], entre outros. O comportamento dinâmico do VSS pode ser modelado utilizando um MFIS para obtenc ¸ão de um bom desempenho do algoritmo NLMS independente de medidas estatísticas de alta ordem.…”

Section: Introduc ¸ãOunclassified

Metodologia de Equalização Adaptativa de Canais de Comunicação via Algoritmo NLMS com o Tamanho de Passo Variável Fuzzy

Noronha¹

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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A equalização é uma importante aplicação da teoria de processamento de sinais para a teoria comunicação, uma vez que sua função é cancelar ruídos presentes em canais de comunicação. O desempenho de um equalizador adaptativo é influenciado pelo tamanho de passo de um algoritmo adaptativo baseado em gradiente descendente estocástico, utilizado para atualização da estimativa do vetor de pesos. Um bom desempenho na equalização adaptativa pode ser obtido através da adaptação do tamanho de passo variável. Para isso, neste artigo é proposta uma nova versão do algoritmo NLMS com o tamanho de passo variável adaptado por um Sistema de Inferência Fuzzy Mamdani, aplicado na equalização adaptativa de canais de comunicação. O equalizador adaptativo foi analisado em três cenários de relação sinal-ruído com SNR = 18 dB, SNR = 24 dB e SNR = 30 dB.

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Mamdani based Fuzzy Logic Controller for A Wheeled Mobile Robot with Obstacle Avoidance Capability

Cited by 6 publications

References 10 publications

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Basic structure of the neurofuzzy control system for a group of mobile robotic platforms

Metodologia de Equalização Adaptativa de Canais de Comunicação via Algoritmo NLMS com o Tamanho de Passo Variável Fuzzy

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