ANYBODY CAN BUILD AN AUTONOMOUS MACHINE

HIGH-TECH SYSTEMS 2017

EindhovenMar. 16th, 2017

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Flanders Make offers you

international innovation network

technological research

research infrastructure

Innovation through collaboration

VALORISATION

PARTNERS

MANUFACTURING

INDUSTRY

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Autonomous cars/machines:Why is it so hard?

They have to deal with ALL possible situations

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Towards autonomy

How do we solve this now?

not so autonomous (human interventions)

restricted environments (factories, fields, greenhouses, …)

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How do we (humans) do it?

Create a virtual world (or map) from sensory inputs

Discern where we can walk and where not

Imagine a path past fixed obstacles

Global but rough, locally detail when we get there

Predict and avoid moving things

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Break down the problem

Impossible to solve this problem at once (in real-time)

So we break it down in “digestible” chunks

SENSORS

FIXED OBSTACLES

MOVING OBSTACLES

VIRTUAL WORLD

GLOBAL STATIC PATH PLANNING

MOTION CONTROL

LOCAL DYNAMIC PATH PLANNING

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Static virtual world and pathplanning

SENSORS

FIXED OBSTACLES

MOVING OBSTACLES

VIRTUAL WORLD

GLOBAL STATIC PATH PLANNING

MOTION CONTROL

LOCAL DYNAMIC PATH PLANNING

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Spatial tree structure (based on “quadtree”)

Iterative division of the 2D map into smaller quadrants

Quadmap

root

0 1

2 3

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12 13

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0 1 2 3

0 1 2 3

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Quadmap

Multi-resolution views

Custom resolution depth

Refinement along path

Example: harbor of Zeebrugge

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Navigation graph

Fast generation from

quadmap

All possible path

segments within the

chosen resolution

Auto-adjusts

together with

quadmap

Path planning with

Dijkstra

Developed time-

dependent variant of

Dijkstra

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Local dynamic path planning

SENSORS

FIXED OBSTACLES

MOVING OBSTACLES

VIRTUAL WORLD

GLOBAL STATIC PATH PLANNING

MOTION CONTROL

LOCAL DYNAMIC PATH PLANNING

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Motion trajectory

●Described by functions x(t), y(t)

Described by functions x(t), y(t)

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Spline approach

Approach x(t) as sum of bases

If weights are positive → x(t) > 0 certified

Constraints can be defined as constraints on the parameters

e.g. wall on y = -1

1A+0.2 B+ 3 C

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In action

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Virtual world – dynamic layer& Sensor fusion

SENSORS

FIXED OBSTACLES

MOVING OBSTACLES

VIRTUAL WORLD

GLOBAL STATIC PATH PLANNING

MOTION CONTROL

LOCAL DYNAMIC PATH PLANNING

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Plato's cave

Ideal object Observation

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Ideal object

Observation

XYWHΘ

Parameter space Observation space

Fitting – OptimatchTM

OPTIMATCH• Play around in parameter space• Find best possible fit

Forward and reverse mapping

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OptimatchTM

Tracking of objects in combination with a Kalman filter

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OptimatchTM

Direct use of Optimatch for fusion of multiple sensors

Other usage: 3D localization from multiple cameras

E.g. strawberry localization

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Project Video

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Questions?