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An Article Review by Matthew D. Schrieber

Implementation of Human-Like Driving Skills by Autonomous Fuzzy Behavior Control on an FPGA-Based Car-Like Mobile Robot by Tzuu-Hseng S. Li, Shih- Jie Chang and Yi-Xiang Chen. An Article Review by Matthew D. Schrieber. Outline. Article Objectives Overview of Different Controllers

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An Article Review by Matthew D. Schrieber

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  1. Implementation of Human-Like Driving Skills by Autonomous Fuzzy Behavior Control on an FPGA-Based Car-Like Mobile Robotby Tzuu-Hseng S. Li, Shih-Jie Chang and Yi-Xiang Chen An Article Review by Matthew D. Schrieber

  2. Outline • Article Objectives • Overview of Different Controllers • Simulated Results • Circuit Design on an FPGA • Real-Time Experiment Results • Contributions • Comments and Criticisms

  3. What is a Car-Like Mobile Robot (CLMR)? • The rear wheels are fixed parallel to the car body • The front wheels can turn to the left or right, but must remain parallel to each other

  4. What are Human-Like Driving Skills?

  5. What is Fuzzy Logic? • Computer logic based on Degree of Membership (DOM) ranging from 0 to 1 instead of true or false (0 or 1) • Membership functions are used to describe variables. • Uses linguistic variables to describe the partitions of the membership function Retrieved from: http://en.wikipedia.org/wiki/Fuzzy_control_system

  6. What is a Fuzzy Logic Controller (FLC)? • Preforms that same function of a traditional controller • Inputs require fuzzification and outputs require defuzzification to become crisp again • Uses specific rule sets to determine output eg. IF temperature IS cold THEN heater IS on Retrieved from: http://en.wikipedia.org/wiki/Fuzzy_control_system

  7. Article Objectives • To merge the concepts of car maneuvers, fuzzy logic controls, and sensor-based behaviours • Implement an autonomous car-like mobile robot capable of human-like driving skills

  8. Autonomous Fuzzy Behaviour Controller The CLMR’s behaviour is broken down into the following 4 different fuzzy controllers: • Fuzzy wall-following control (FWFC) • Fuzzy corner control (FCC) • Fuzzy garage-parking control (FGPC) • Fuzzy parallel-parking control (FPPC) Notes: • The FWFC is the only controller that is entirely fuzzy based • The other controllers have addition algorithms that use the FWFC.

  9. Fuzzy Wall-Following Control

  10. Fuzzy Wall-Following Control Steering Angle • Forward Motion • Backward Motion Speed Control

  11. Fuzzy Wall-Following Control Membership Functions • Steering Angle Control • Speed Control

  12. Fuzzy Wall-Following ControlFuzzy Rule Tables Steering Angle Control Speed Control

  13. Fuzzy Corner Control

  14. Fuzzy Garage-Parking Control

  15. Fuzzy Parallel-Parking Control

  16. Behaviour Selection Mechanism The wall-following mode is the default behaviour, however the behaviour will switch depending on the measured values of the sensors. The selection rules are as follows: > > >> >>

  17. Simulated Results Notes: • It was found that the CLMR could not correctly drive into the parking space if the membership function for the steering angles were partitioned into 3 instead of 5. • There was no obvious different between 5 and 7 partitions.

  18. Hardware Architecture FPGA: Altera Flex series manufactured by Galaxy Far East CAD Tool: MAXPLUS II Sensors: UF 66 MG manufactured by TELCO International A/D Converter, D/A Converter and Motor Driver IC.

  19. Circuit Design on an FPGA Inputs pins: 8 for each sensor via A/D converter and 1 for the clock Output pins: 2 for DC motor driver, 1 for A/D converter, 1 for DC servomotor, 8 for the D/A converter

  20. Behaviour Selection Control Module • Uses a Mux to implement the Behaviour Selection Mechanism described earlier • Both outputs describe whether the CLMR is going forwards or backwards

  21. Pulse-Width Modulation Fitness Module • Used for controlling the DC servomotor that controls the steering angle. • Using the Crisp output from the FLC

  22. Design of FLC Module • Steering angle and speed control both use this module

  23. FuzzificationSubmodule • Sensor-based inputs • “Choice” signal indicates whether CLMR should go forward or backward

  24. Decision-Making Logic Submodule • The inference rule base block is realized in a lookup table

  25. DefuzzificationSubmodule • Done by using the Weighted Average Method • Parallel multiplier used to speed up performance

  26. Real–Time Experimental Results Capable of autonomously maneuvering in their test ground which includes garage and parallel parking CLMR Dimensions: • Length 380mm • Width 240mm • Weight 5kg

  27. Contributions • Great foundation work for applying Fuzzy Logic Controller to mobile robotics • Autonomous behavior controllers capable of human-like driving skills are becoming features in modern cars (Automatic Parallel Parking)

  28. General Comments and Criticisms • Proficiency in English was lacking • Many decisions lacked justification • Significant details about the development of many of the individual components was lacking • Impressive merger of many different design concepts

  29. Specific Comments and Criticisms • The functionality of the Behaviour Selection Mechanism is unclear. • Other portions of the different controllers could have been implemented using fuzzy logic instead of just the FWFC. • The simulated and experimental results are impressive and the varying of the membership function partitions was insightful.

  30. Questions?

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