A genetic-fuzzy approach for mobile robot navigation among moving obstacles

Pratihar, Dilip Kumar; Deb, Kalyanmoy; Ghosh, Amitabha

doi:10.1016/s0888-613x(98)10026-9

Cited by 108 publications

(49 citation statements)

References 24 publications

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“…The obstacle avoidance behavior is done by range finding sensors, which detects the nearest obstacle distance, and the goal-seeking behavior is made by compass measurements, which determines the direction of the goal. Pratihar et al [44] have developed a genetic-fuzzy technique based on a combined approach of genetic algorithm and fuzzy logic (GA-FL) to solve the mobile robot motion planning problems in the dynamic environments. Sensor-based wireless fuzzy controller has been designed by Faisal et al [45] for mobile robot navigation in the industries among the static and dynamic objects.…”

Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

161

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Section: Hybridization Of Fuzzy and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

161

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“…EAs have been used in such diverse fields as Economics and Social Theory (Axelrod, 1987), Robotics (Pratihar et al, 1999) and Art (Sims, 1991). For many nontrivial real-world applications the evaluation of the objective function is performed by computer simulation of the system.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Optimisation of Maintenance Scheduling Strategies on the Grid

Shenfield¹,

Fleming

Allan

et al. 2007

2007 IEEE Symposium on Computational Intelligence in Scheduling

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The emerging paradigm of Grid Computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a Grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise, achieving substantial improvements in the execution time of the algorithm.

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“…A genetic learning algorithm has been proposed to optimize the type-2 fuzzy neural network parameters [20] [21]. GA has been used in tuning both the fuzzy MFs and the fuzzy rule bases [22] [23] in the areas of mobile robotics. The optimization of the MF parameters of a type-2 Mamdani FLS was presented in [24] based on fuzzy genetic architecture.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

El-Gammal¹

2014

JILSA

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This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator-and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

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A genetic-fuzzy approach for mobile robot navigation among moving obstacles

Cited by 108 publications

References 24 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Optimisation of Maintenance Scheduling Strategies on the Grid

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

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