incorporation of matlab into a distributed behavioral robotics architecture

IROS 2004, Sendai, JapanResearch deploy train evaluate

Incorporation of MATLAB into a Distributed Behavioral Robotics

Architecture

A. L. Nelson, L. Doitsidis, M. T. Long, K. P. Valavanis, and R. R. Murphy


Overview

• Introduction

• The Distributed Field Architecture

• Robots and Hardware

• Example Uses– Basic robot sensor error quantification in outside

environments– Waypoint navigation with object avoidance

• Conclusions


Introduction• Current robot research demands a versatile

control architecture

– Heterogeneous Robots– Outdoor environments– Distributed Control– Autonomous Control– Shared Autonomy


Introduction

• Motivation– Provide a unified versatile multirobot research

platform– Support AI and Control Theoretic work– Unify robot control research and development

phases for continuity and reduced development time

• Behavior-based robot control architectures


Introduction: Related Work• S. Monteiro, E. Bicho, E. “A dynamical systems approach to

behavior-based formation control,” Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on, vol. 3, 2002, pp. 2606 – 261.

• O. Ewerlid, C. Tidestav and M. Sternad, “Real Time Control using Matlab and Java,” Nordic Matlab Conference, Stockholm, October 27-28, 1997.

• A. L. Nelson, E. Grant, T.C. Henderson, “Evolution of neural controllers for competitive game playing with teams of mobile robots,” Journal of Robotics and Autonomous Systems, vol. 46, no. 3, pp. 135-150, Mar 2004.


• Distributed • Java-based• Descendant of SFX• Behavior based• Hybrid deliberative

reactive architecture

The Distributed Field Architecture


MATLAB Support

• JMatLink Modular Support for MATLAB

• Decupling of client and server

• Modules• MATLAB is shown as a

driver implementation module

Module

Remote Module

Driver

MATLAB

Proxy

Jmatlinknative

Server

JMatlink

Delegates to

Native Layer

Delegates to

uses


MATLAB Support

• MATLAB runs as full work space– Interpreted functions

and scripts– Workspace command

line strings– All tool boxes

• Workspace accessed by JMatLink with formatted strings

...matlink.engPutVariable(engine, “laserData” ,

laserData.readLaser);matlink.engPutVariable(engine, “gpsData”,

gps.readGps);

matlink.engEvalString(engine, “sensorData. laserData = laserData”);

matlink.engEvalString(engine, “sensorData.gpsData = gpsData”);

matlink.engEvalString(engine, “result = mFunction(sensorData.”);

resultData = engGetVariable(engine, “result”);....


MATLAB Support

• MATLAB Usage modes:

– Development Phase

– Production PhaseJmatlinknative

JMatlink

Write Data to MATLAB(Sensors readings,

Percepts)

Read Data From MATLAB(Actuator Commands,

Processed Data)

MATLAB Module

MATLAB Module

MATLAB Module(Developed Service

Implementation)

MATLAB Workspace

Control LoopBody

MATLAB Module

Java/Jini

Research Experimentation andModule Development Tool

Distributed SFX

Production Phase Development Phase

Completed serviceimplementation

(interaction with the largerDistributed SFX architecture,

see Fig. 1)


Robots and Hardware

Heterogeneous outdoor robots


Application: Basic Sensor Error Characterization

• GPS points and points calculated from odometry for an example linear test pattern.

-10 -5 0 5 10 15 20

-20

-15

-10

-5

0

5

Start

West --- East (m)

South --- North (m)

Odometry

Filtered GPS

Unfiltered GPS

Linear Test Pattern


Application: Basic Sensor Error Characterization

-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4

-12

-10

-8

-6

-4

-2

0

2

4

6

8

Start

West --- East (m)

South --- North (m)

Rectangular Test Pattern

Odometry

Filtered GPS

Unfiltered GPS

-30 -25 -20 -15 -10 -5 0 5 10 15 20

-30

-25

-20

-15

-10

-5

0

5

10

Start

West --- East (m)

South --- North (m)

Circular Test Pattern

Odometry

Filtered GPS

Unfiltered GPS


Ongoing Research

• Go to goal with obstacle avoidance


Example Controller Block Diagram

Laser RangeFilter

(uses current and past laserscans and odometry)

Filtered LaserRange

MotorolaProprietaryGPS Filler

FilteredGPS

GPS(lat, lon)

(Fuzzy ControllerInputs)

Robot MotorDrive System

Robot PositionDetection

(uses current and past GPSreadings and odometry)

Heading ErrorCalculation

Current Target Waypoint(lat, lon)

Odometer(iRobot Mobility Virtual

Encoder output)

Laser scan

Conversion toFuzzy inputvariables:

Left Range Center Range Right Range

FuzzyControllerRule Base

Position(lat, lon, angle)

Heading AngleError (e)

Left

Center

Right

(Fuzzy Control Outputs:Motor Commands)

Translational Velocity(v)

Rotational Velocity(’)

(Feedback via Environment)

Controller

PlantSensorInputs

System Inputs

LaserPrefilter


Additional Experiments

• Example: Fuzzy Control– Multiple Robots – Obstacle avoidance


Conclusions

• A robot control architecture for advanced research was presented

• Combined high-level control and modeling environment and a distributed behavior-based architecture

• Example usages demonstrate utility of the overall system presented


Acknowledgements

• This work was partially supported by a grant form ONR, N 000 14-03-1-786 (2132-033-LO).

• L. Doitsidis was partially supported by “IRAKLITOS fellowships for research from the Technical University of Crete, EPEAEK II – 88727

incorporation of matlab into a distributed behavioral robotics architecture

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