Fuzzy control of a mobile robot

Implementation using a MATLAB-based rapid prototyping system

Intro

• Finding• That autonomous robot performs complex tasks in unknown or

semiunknown enviroment:» Industrial automation

» Exploration of hazardous environments (mines)

• Having• Low cost sensors and actuators:

» Infrared sensors

» Camera

» Motors

• Fuzzy Logic» Based on relative to observed

» Good on dynamic environments

Main problem

• It is difficult to give a comprehensive description of unstructured navigation environments

• It is difficult to effectively take into account all the details of the unknown scenarios

• Robot behaviours are based on simulations

• Simulations cannot easily take into account effects like nonlinearities, noise, uncertainties...

Solution

• A flexible and modular hardware platform that allows to design and validate the fuzzy control algorithms

• Hardware platform allows:

• Real time control of low cost sensor equiped robots

• Exploits MATLAB/Simulink programming enviroment

• It provides a tool for developing user-friendly graphical interfaces

• Combines modular hardware and transparent software

Platform architecture

• Mobile robot: Khepera (slave)» 2 wheels» 8 (noisy) infrared» RS232 serial link (interactive control)» Flash memory

• dSPACE:– Microcontroller board working as interface

between PC and robot (RS232)– Fully programable in a MATLAB enviroment– Real time communication between board and

MATLAB routines running on the PC

• A terminal (master)– 8 bit ASCII based comunication with Khepera

• Comunication– Command from terminal to Khepera– Response from Khepera to terminal

Platform architecture

• Topview Webcam:• USB interface

• Images from Webcam are processed directly into MATLAB using color detection codes

• 1.2 x 1.2 m arena is entirely viewed by the Webcam

Fuzzy decision and control algorithm design

– Obstacles, dynamics and statics, position unknown– Target position unknown– Navigation implemets reactive algorithm to get the target and avoid

obstacles using only sensory information– Three simple behaviors:

• Reach the target» Artificial vision

» Primary task

• Avoid obstacles» Infrared

» Highest priority

• Explore the enviroment» Spatial local memory

» Avoid visiting already visiting regions

– Modular arquitecture allows faster debugging and tunning and to easily add new behaviors

Fuzzy decision and control algorithm design

Behaviors: Reach the target

– Provides information about position between robot and target– Don’t care about obstacles– Khepera is marked with two colored markers, target with a red spot.

Behaviors: Reach the target

– Image information is passed to FLC1 in dSpace board every 200ms– Robot turns to face the target and moves straight– Distance to target (DIR) and aligment (DIR) is processed by the vision

system (PC) and passed to FLC1

– Labels for fuzzy sets:• DIST

» zero

» near

» far

• DIR» left

» center-left

» center

» center-right

» right

Behaviors: Reach the target

– Output: speed commands for wheels:» negative fast

» negative slow

» zero

» positive slow

» positive fast

Behaviors: Avoid obstacles

– IR Khepera sensors are labeled from S0 to S7– Labels for fuzzy sets:

» far

» approaching

» close

» colliding

Behaviors: Explore the enviroment

– Arena is divided into a matrix of 25 mm x 25 mm each grid– Each visit to a grid, their value is increased in one– Inputs from image processing to FLC3– Inputs for delta (north, south, east, west):

» more explored

» less explored

Fuzzy supervision

– Determines priority of execution for the behaviors

– Labels for fuzzy sets:– Exp_Ind

» often

» seldom

– Max_prox» close

» far

Experiments

– 1.2 x 1.2 m arena– White obstacles are undetectable to the camera– Red spot is the target

Experiments

– Box canyon in the straight way is avoided– It takes 80 seconds (times and behavior coloured traced)

Conclusions

– The experience of the design of the behavior-based navigation confirms the potential of fuzzy-logic, overcoming inherent limitations of low-cost hardware.

– The prototyping platform simplify and enhance the design proccess.

– The MATLAB/Simulink software package free the designers from low-level hardware and software issues, giving a good chance to educational purposes.

– Main limitations of the proposed platform lie in the speed of the serial communication

Query time

???