A Performance and Schedulability Analysis of an Autonomous Mobile Robot Jiangyang Huang & Shane...

A Performance and Schedulability Analysis of an Autonomous Mobile Robot

Jiangyang Huang & Shane FarritorMechanical Engineering

University of Nebraska–Lincoln

Ala’ Qadi & Steve Goddard Computer Science & Engineering University of Nebraska–Lincoln

Highway Robotic Safety Marker System RSM system is a mobile,

autonomous, robotic, real-time system that automates the placement of highway safety markers in hazardous areas.

The RSMs operate in mobile groups that consist of a single lead robot (the foreman) and worker robots (RSMs).

prototype foreman.A prototype RSM

Tasks Performed by the Foreman Plan its own path and motion.

Locate RSMs, plan their path, communicate destinations points, and monitor performance.

Foreman Design

Power Unit

Batteries, DC to DC

converters, etc.

Sonar Unit

24-sonar ringcircuit board

Communication Unit

9XStream OEM RF Module

Motor Unit

PIC16F84MicroController

Motor Circuit Board

Driving Motor

Steering Motor

DM5406

Main Unit

PC/104-Plus(Windows CE OS)

Parallel Port

RS232

RS485

TCP/IP

Sensor Unit

Rabbit 3000 Microprocessor, encoders, gyro

Localization Unit

Sick Laser LMS200

Foreman Path Planning

Plan its own path and motion. Sonar sensors are used to

detect obstacles in the foreman’s path.

The sonar unit consists of a

ring of 24 active sonar sensors, with 15 separation, that provides 360 coverage around the foreman.

1515

15

15 1

51

5151515o

Sonar Sensor Distribution


Sonar Send Task: Sends a command to its corresponding sonar sensor to transmit its signal.

Sonar Receive Task: Reads the corresponding sonar sensor after the signal is echoed back to the sensor.

Motor-Control Task: Computes the path of the foreman and controls its speed based on the data collected from the sonar signals.

Foreman Path Planning Task Set

Foreman Motion Control Task Set

Task e P d max J

Sonar-Sendi esend=.085ms ps esend sendi 0

Sonar-Receivei erecv=.03ms ps esend+ erecv+ max t recvi max t

Path-Plan-Speed-Control

eplan=1.32ms ps eplan plan 0

SystemStart

. . .

Motion Start

. . .

vmax.ps vmax.ps vmax.ps

D = Dmax(Sonar Range)



vmax.ps

S0S1

S2 S3 S4

Zone 1Zone 0 Zone 2

Zone 0*

Zone 3

ps

ps

ps

ps

ps

Time

TraveledDistance(0,0)

Case 1: Ideal Environment (No Obstacles)



SystemStart

. . .

Motion Start

. . .

vmax.ps vmax.ps vmax.ps




vmax.ps

S0S1

S2 S3 S4

Zone 1Zone 0 Zone 2

Zone 0*

Zone 3

ps

p sp

sp s

p s

Time

TraveledDistance(0,0)

planrecvsend

plansendrecvsend

planrecvrecvs

eesm

De

eeete

edp

/340

22523

max24232424

Case 1: Ideal Environment (No Obstacles)

1

1max

i

i

i

s

sii

p

pvDv


Maximum Safe Distance Depends on the obstacles.

Speed might need to be adjusted at scan points due to obstacles.

This means that the maximum speed for the zone after the obstacle is also dependent on the speed before reaching the obstacle.

Case 2: Obstacles Exist

Example Scenario 1 D=Dmax , No period adjustments

Example Scenario 2Period adjustments and Sonar Range Adjustments

Locate RSMs, plan their path, communicate destinations points, and monitor performance. A laser scanner is used to determine the location of the RSMs.

RSM Motion Planning and Tracking

Task e p d max J

Scanning 12ms pl pl 0 0

Detecting .0172n2+.1695n+12.69 pl pl 0 0

Predicting 3.8n pl pl 0 0

Planning 16ms 1500ms 1500ms 0 0

Way Point 8.33ms 1500ms 1500ms 0 0

Window Resizing

2ms pl pl 0 0

RSM Motion Planning and Tracking Task Set

Relation Between RSM Location Estimation Error and the Laser Scan Period

0

10

20

30

40

50

60

70

80

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000

Laser Sampling Rate (ms)

Dist

ance

Err

or (c

m)

0

5

10

15

20

25

0 100 200 300 400 500 600 700 800 900 1000

Laser Sampling Rate (ms)

Dist

ance

Err

or (c

m)

Average Error Maximum Error

467.12616.0

2517.00219.0

max

laverage p

Characteristics of the Task Set

Some tasks have variable periods that depend on the system performance parameters.

The accuracy of RSM position prediction is dependant in pl. (Higher accuracy with smaller period.)

The foreman’s maximum traveling speed is dependant on ps. (Higher speed means smaller periods.)

Combine both task sets into one task set with fixed priority.

Analyze the task set and devise the minimum number of online scheduling tests with minimum overhead.

Combined Task Set

Task Index

Task p Priority

1 Sonar Sendi ps 1

2 Plan Speed ps 2

3 Sonar Receivei ps 3

4 Scanning pl 4

5 Detecting pl 5

6 Predicting pl 6

7 Window Resizing

pl 6

8 Planning 1500 7

9 Way Pointi 1500 8

Proposed Solution

Offline Tests Theorem 4.1: All Sonar Send tasks (Task 1) will

always their deadlines if .max2423 plansendrecvsends eeetep

Task Set Analysis



.max2423 plansendrecvsends eeetep

Task Set Analysis

Theorem 4.2: The Path-Plan/Speed-Control task (Task 2) will always meet their deadlines if





Task Set Analysis

Theorem 4.2: The Path-Plan/Speed-Control task (Task 2) will always meet their deadlines if

Theorem 4.3: All Sonar Receive tasks (Task 3) will always their deadlines if

Online Tests Theorem 4.4: All tasks will meet their deadlines if

Equations (9) and (10) hold.

(9)

(10)

plplDEMeii

i

Tpp

i

)(3,...1,

1500)1500(3,...1,

1500

iiT

pi DEMe

Task Set Analysis

Period Adjustments

Task periods pl and/or ps may need to be adjusted to achieve the desired performance metrics in the following cases: Adjusting the speed of the foreman—either because

we want to move faster from one position to the other or because there is an obstacle in the path.

Increasing the accuracy of RSM path prediction. Increasing the number of RSMs being controlled.

On Going And Future Work Developing an application-level control

algorithm that can make dynamic performance/schedulability tradeoffs.

Generalizing the modeling and schedulability analysis presented here so that it can be applied more easily to tasks of other real time mobile autonomous systems.

Questions??

A Performance and Schedulability Analysis of an Autonomous Mobile Robot Jiangyang Huang & Shane...

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