Robotics & Sensor Fusion for Mechatronics Autonomous vehicle navigation An Obstacle Avoidance...

Robotics & Sensor Fusion for Mechatronics

Autonomous vehicle navigation

An Obstacle Avoidance Exercise

Luca Baglivo, Mariolino De Cecco


We’re using two-dimensional grids: maps represented as images!


From CAD to Image


ATTRACTIVE POTENTIAL REPULSIVE POTENTIAL+

IMAGINE ROBOT AS A BALL ROLLING DOWN HILLS


TOTAL POTENTIAL


THE RESULTING FORCE IS THE GRADIENT AND GIVES DIRECTION TO THE ROBOT

This example is in the Matlab script “OstacoliQuadrati.m”


POTENTIAL FIELDS METHOD FEATURES:

• AUTOMATIC PATH PLANNING FOR OBSTACLE AVOIDANCE

• IS BOTH A PLANNING & CONTROL STRATEGY ALL-IN-ONE

• BEST FOR LOCAL PATH PLANNING->UNEXPECTED OBSTACLES

• BE AWARE FROM LOCAL MINIMA! HARMONIC POTENTIAL

FUNCTIONS HAS PROVEN ONLY GLOBAL MINIMA

• NOT SUITABLE FOR HIGH PRECISION POSITIONING ON TARGET


A FORMULATION


ANOTHER, NAIVE FORMULATION

A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING

• The attractive potential can be defined punctually as desired.

• Build a vector field that point towards desired path.



A VIRTUAL CORRIDOR ALIGNMENT FOR LINE FOLLOWING

• How to define it

Lc

xF

yF

K yangles (+)

alphaK




• How to compute steering angle input

K y

alphaK delta

steering axis




• Now add the repulsive force vector Frep, and play …

K y

Frep Ftot

delta’


OBJECT PICKING

A possible application for forklifts




• Try with:

• Tricycle robot forward velocity, point obstacle at (xF,yF) = (4,1.5)

yR

b

D1




• A control sketch

Robot kinematic

model

Steer control

velocity

steer angle

Potential field gradient vector

y

+ -alphaK

delta

kcontrol


Bibliography

1. Siegwart R., Nourbakhsh I, Scaramuzza D., Introduction to Autonomous Mobile Robots

Robotics & Sensor Fusion for Mechatronics Autonomous vehicle navigation An Obstacle Avoidance...

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