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

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

Enabling fuzzy technologies in high performance networking via an open FPGA-based development platform

Soft computing techniques and particularly fuzzy inference systems are gaining momentum as tools for network traffic modeling, analysis and control. Efficient hardware implementations of these techniques that can achieve real-time operation in high-speed networking equipment as well as other highly time-constrained application fields is however an open problem. We introduce a development platform for fuzzy inference systems with applications to network traffic analysis and control. The platform addresses the current requirements and constraints of high performance networking equipment. For the development process, we set up a methodology and a CAD tool chain that span the entire design process from initial specification in a high-level language to implementation on FPGA devices. An FPGA development board with PCI/PCIe interface is employed to support an open platform that comprises CAD tools as well as IP cores. PCI compatible fuzzy inference modules are implemented as System-on-Programmable-Chip (SoPC). We present satisfactory experimental results from the implementation of fuzzy systems for a number of applications in analysis and control of Internet traffic. These systems are shown to satisfy operational and architectural requirements of current and future high performance routing equipment. The platform proposed allows for the development of prototypes while avoiding large investments and complicated management procedures which constrain the testing and adoption of soft computing techniques in high performance networking. © 2011 Elsevier B.V. All rights reserved.This work was done while the first author was at IMSE-CNM. This work was supported in part by the European Community under the MOBY-DIC Project FP7-IST-248858, by Spanish Ministerio de Ciencia y Tecnología under the Project TEC2008-04920, and by Junta de Andalucía under the Project P08-TIC-03674, co-financed by FEDER.Peer Reviewe