Neuro-fuzzy techniques to optimize an FPGA embedded controller for robot navigation

被引:15
作者
Baturone, Iluminada [1 ,2 ]
Gersnoviez, Andres [3 ]
Barriga, Angel [1 ,2 ]
机构
[1] Univ Seville, Dept Elect & Electromagnetismo, Seville, Spain
[2] Inst Microelect Sevilla IMSE CNM CSIC, Seville, Spain
[3] Univ Cordoba, Dept Arquitectura Comp Elect & Tecnol Elect, Cordoba, Spain
关键词
Neuro-fuzzy techniques; Embedded systems; FPGA implementation; Intelligent control; Robot navigation; CAR-LIKE ROBOT; MOBILE ROBOT; DESIGN; SYSTEMS; IMPLEMENTATION; IDENTIFICATION; PATHS;
D O I
10.1016/j.asoc.2014.03.001
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
This paper describes how low-cost embedded controllers for robot navigation can be obtained by using a small number of if-then rules (exploiting the connection in cascade of rule bases) that apply Takagi-Sugeno fuzzy inference method and employ fuzzy sets represented by normalized triangular functions. The rules comprise heuristic and fuzzy knowledge together with numerical data obtained from a geometric analysis of the control problem that considers the kinematic and dynamic constraints of the robot. Numerical data allow tuning the fuzzy symbols used in the rules to optimize the controller performance. From the implementation point of view, very few computational and memory resources are required: standard logical, addition, and multiplication operations and a few data that can be represented by integer values. This is illustrated with the design of a controller for the safe navigation of an autonomous car-like robot among possible obstacles toward a goal configuration. Implementation results of an FPGA embedded system based on a general-purpose soft processor confirm that percentage reduction in clock cycles is drastic thanks to applying the proposed neuro-fuzzy techniques. Simulation and experimental results obtained with the robot confirm the efficiency of the controller designed. Design methodology has been supported by the CAD tools of the environment Xfuzzy 3 and by the Embedded System Tools from Xilinx. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:95 / 106
页数:12
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