Multiobjective Evolutionary Fuzzy Modelling in Mobile Robotics
نویسندگان
چکیده
In some environments, mobile robots need to perform docking tasks in a precise manner. In the application domain of this work, an Autonomous Guided Vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. In these maneuvers, the robot motion should be controlled accurately when the mobile robot is close to the target. We propose a multiobjective evolutionary algorithm in order to find multiple controllers with imposed constraints for docking task in charge of following up an online generated trajectory. Main purpose is to improve some features of docking task as its duration, accuracy and stability, satisfying determined constraints.
منابع مشابه
Multiobjective Imperialist Competitive Evolutionary Algorithm for Solving Nonlinear Constrained Programming Problems
Nonlinear constrained programing problem (NCPP) has been arisen in diverse range of sciences such as portfolio, economic management etc.. In this paper, a multiobjective imperialist competitive evolutionary algorithm for solving NCPP is proposed. Firstly, we transform the NCPP into a biobjective optimization problem. Secondly, in order to improve the diversity of evolution country swarm, and he...
متن کاملThe Position of Multiobjective Programming Methods in Fuzzy Data Envelopment Analysis
Traditional Data Envelopment Analysis (DEA) models evaluate the efficiency of decision making units (DMUs) with common crisp input and output data. However, the data in real applications are often imprecise or ambiguous. This paper transforms fuzzy fractional DEA model constructed using fuzzy arithmetic, into the conventional crisp model. This transformation is performed considering the goal pr...
متن کاملThe Role of Fuzzy Logic Control in Evolutionary Robotics
This paper presents an evolutionary learning algorithm to facilitate the design of fuzzy controllers for mobile robots. It discusses the concepts, feasibility, bene ts and limitations of current evolutionary techniques for fuzzy rule discovery and tuning. We propose an evolution strategy that optimizes the gain factors in the conclusion part of TakagiSugeno-Kang type fuzzy rules. We describe tw...
متن کاملInteractive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference
One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two conflicting objectives (i.e., accuracy maximization and complexity minimization) when we design accurate and compact fuzzy rule-based systems from numerical data. Since the main advantage of fuzzy rule-based systems co...
متن کاملA Multi-Objective Genetic Algorithm Approach to Feature Selection in Neural and Fuzzy Modeling
A large number of techniques, such a neural networks and neurofuzzy systems, are used to produce empirical models based in part or in whole on observed data. A key stage in the modelling process is the selection of features. Irrelevant or noisy features increase the complexity of the modelling problem, may introduce additional costs in gathering unneeded data, and frequently degrade modelling p...
متن کامل