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Autonomous vehicle navigation An Obstacle Avoidance Exercise
Luca Baglivo, Mariolino De Cecco
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We’re using two-dimensional grids: maps represented as images!
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Example from CAD to Image
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Let’s consider the following simplified scenario:
Goal Start
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+ ATTRACTIVE POTENTIAL REPULSIVE POTENTIAL
… AND IMAGINE ROBOT AS A BALL ROLLING DOWN HILLS
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TOTAL POTENTIAL
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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
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A FORMULATION
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A FORMULATION
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A FORMULATION
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A FORMULATION
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A FORMULATION
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THE RESULTING FORCE IS THE GRADIENT AND GIVES DIRECTION TO THE ROBOT
This example is in the Matlab script “OstacoliQuadrati.m”
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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.
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ANOTHER, NAIVE FORMULATION
A VIRTUAL CORRIDOR ALIGNMENT FOR LINE FOLLOWING How to define it yF alphaK angles (+) y K xF Lc
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ANOTHER, NAIVE FORMULATION
A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING How to compute steering angle input aK d y K steering axis
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ANOTHER, NAIVE FORMULATION
A VIRTUAL CORIDOR ALIGNMENT FOR LINE FOLLOWING Now add the repulsive force vector Frep, and play … Frep Ftot delta’ y K
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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
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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
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Bibliography Siegwart R., Nourbakhsh I, Scaramuzza D., Introduction to Autonomous Mobile Robots
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