Autonomous vehicle navigation An Obstacle Avoidance Exercise Luca Baglivo, Mariolino De Cecco

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

Example from CAD to Image

Let’s consider the following simplified scenario: Goal Start

+ ATTRACTIVE POTENTIAL REPULSIVE POTENTIAL … AND IMAGINE ROBOT AS A BALL ROLLING DOWN HILLS

TOTAL POTENTIAL

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

A FORMULATION

A FORMULATION

A FORMULATION

A FORMULATION

THE RESULTING FORCE IS THE GRADIENT AND GIVES DIRECTION TO THE ROBOT This example is in the Matlab script “OstacoliQuadrati.m”

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.

ANOTHER, NAIVE FORMULATION A VIRTUAL CORRIDOR ALIGNMENT FOR LINE FOLLOWING How to define it yF alphaK angles (+) y K xF Lc

ANOTHER, NAIVE FORMULATION A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING How to compute steering angle input aK d y K steering axis

ANOTHER, NAIVE FORMULATION A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING Now add the repulsive force vector Frep, and play … Frep Ftot delta’ y K

ANOTHER, NAIVE FORMULATION A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING A control sketch y Potential field gradient vector velocity Robot kinematic model alphaK steer angle - delta + Steer control kcontrol

ANOTHER, NAIVE FORMULATION A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING Try with: Tricycle robot forward velocity, point obstacle at (xF,yF) = (4,1.5) yR b D1

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