Hierarchical fuzzy inference system for robotic pursuit evasion task

Hládek, Daniel; Vaščák, Ján; Sinčák, Peter

doi:10.1109/sami.2008.4469182

Cited by 21 publications

(7 citation statements)

References 12 publications

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“…3 are given as the average value of the best five runs of both algorithms. The conclusions can be different for other controllers [26]- [29] and for fuzzy models of other nonlinear processes [30]- [34]. …”

Section: Experimental Validationmentioning

confidence: 85%

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Precup

Sabau

Dragoş

et al. 2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Antilock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.

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Section: Experimental Validationmentioning

confidence: 85%

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Precup

Sabau

Dragoş

et al. 2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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“…Haynes and Sen [13] used genetic programming to evolve strategies for both predators and preys. Hládek et al [14] developed a multiagent control system using fuzzy inference for a group of twowheeled mobile robots to execute a common task. They defined a pursuit-evasion task using fuzzy sets to establish a framework for inserting and updating expert knowledge in the form of rules by an inference system.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Once the pursuers can cooperate with each other, the pursuers randomly select one of them as a leader. Other pursuers can determine their locations at time + 1 by the following (14): (14) where ( + 1) is the location of the leader. To avoid pursuers moving to the same location, each pursuer finds the minimum overlap area.…”

Section: Local-cooperativementioning

confidence: 99%

Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

Kuo

Liu

et al. 2015

Mathematical Problems in Engineering

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This study examines the pursuit-evasion problem for coordinating multiple robotic pursuers to locate and track a nonadversarial mobile evader in a dynamic environment. Two kinds of pursuit strategies are proposed, one for agents that cooperate with each other and the other for agents that operate independently. This work further employs the probabilistic theory to analyze the uncertain state information about the pursuers and the evaders and uses case-based reasoning to equip agents with memories and learning abilities. According to the concepts of assimilation and accommodation, both positive-angle and bevel-angle strategies are developed to assist agents in adapting to their environment effectively. The case study analysis uses the Recursive Porous Agent Simulation Toolkit (REPAST) to implement a multiagent system and demonstrates superior performance of the proposed approaches to the pursuit-evasion game.

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“…Fuzzy-Rule-Based Systems (FRBSs) have been applied in applications such as control engineering, expert systems, pattern recognition, operation research, and decision support systems [1][2][3][4][5][6][7]. FRBS output is generated by an inference mechanism based on a knowledge base of IF-THEN rules.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

Tikk

Johanyák

Kovács

et al. 2011

J. Adv. Comput. Intell. Intell. Inform.

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This paper comprehensively analyzes Fuzzy Rule Interpolation and extrapolation Techniques (FRITs). Because extrapolation techniques are usually extensions of fuzzy rule interpolation, we treat them both as approximation techniques designed to be applied where sparse or incomplete fuzzy rule bases are used, i.e., when classical inference fails. FRITs have been investigated in the literature from aspects such as applicability to control problems, usefulness regarding complexity reduction and logic. Our objectives are to create an overall FRIT standard with a general set of criteria and to set a framework for guiding their classification and comparison. This paper is our initial investigation of FRITs. We plan to analyze details in later papers on how individual techniques satisfy the groups of criteria we propose. For analysis, MATLAB FRI Toolbox provides an easy-to-use testbed, as shown in experiments.

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Hierarchical fuzzy inference system for robotic pursuit evasion task

Cited by 21 publications

References 12 publications

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems

Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

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