1. 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

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:

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:

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 Control Fuzzy 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. Fuzzification Submodule
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. Defuzzification Submodule
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?