Aditya Ppt on Ann

8/3/2019 Aditya Ppt on Ann

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Neural Navigation Approach forIntelligent Autonomous Vehicles

(IAV) in Partially StructuredEnvironments


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Neural networks for navigation

Neural networks advantages:

Robustness towards noisy data, thus well suitedfor sensorial data processing

Not rule-based: handle wide palette ofenvironments:

Unknown

Complex

Dynamical


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Neural networks robustness

Short lifespan of sensors

Knowledge in NN is distributed over severalneurons, therefore there is redundancy

Noisy data

Classical algorithms fail:

too many cases to handle


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Motion planning environment model

Dynamic Learning from examples

Online learning: continuous weight updates

Unknown / unexplored Learning from examples, able to generalize (this

is interpolation/extrapolation)

Complex No large programming effort and cost

Domain expertise not needed, use training data


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Even more advantages

Neural networks are inherently parallel Parallel implementation is easy

Real-time response adds to robustness becauselate reactions lead to corrective measures


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Compared to classicapproaches

Roadmaps and cell decomposition

Construct data structure to model configurationspace: large data structures and complex

geometric computations

Advantage: can be reused for multiple-query

Potential field methods

In a way, similar to neural network approach Expensive preprocessing and difficult update:

inefficient for dynamic environments


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Motion planning with ArtificialIntelligence techniques?

Neural navigation systems are based on the wayhumans react to their environment:

Humans Neural networksExperience fromobservation

Training fromexamples

Autonomous decisions Generalization

Continuous optimization

Actions Network simulation


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Application of Motion Planning

Motion of an intelligent autonomous vehicle in aunknown, dynamic environment

Known:

Angle towards target

Obstacles in local environment

Global environment

Dynamic and unknown

Data

Low-quality sensors, noisy, occasional failures

Obstacles:

Static, Intelligent (other vehicles), non-intelligent


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System overview

The environment is complex, the system design

must reduce complexity

How?

Reactive: look at local environment only

Hierarchicalstructure


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The system hardware

Vehicles: 5 directions ofmovement

5 possible actions Ai:go forward in

direction i (angle 30, 60, 90, 120,150)

Sensors: 5 ultrasonic sensors

detect the local vehicle-obstacle

configuration range of 0.15-10.5 m

15 degree angle beam


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Hierarchical structure of the NN

Three subtasks:

NN1:target localization T

NN2:obstacle avoidance O

NN3:coordinator; decides action A


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Target localization: NN1

Its a classifier, the output is

a vector indicatingconfidence values for thesectors T1 T6

Input vector XT=(X1,,X5),defines a temperature field,to indicate the direction ofthe target

NN1 must learn whichsector to choose, giveninput temperature vector XT


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Obstacle avoidance: NN2

Also a classifier, theoutput is a particularlocal obstacle

configuration (O1 O30) Input vector

XO=(X1,,X5), Xidenotes minimum

distance to an obstacleat angle i


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Decision-making: NN3

Its the coordinator

Combines outputs of

NN1 and NN2

5 output neurons, thefiring one indicates theaction Ai for the vehicle


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Training

We can train NN1, NN2 and NN3 separately,because of network hierarchy

Speeds up learning

Less training examples needed

Back propagationof the error at the output

Performedincrementally, one example vector

at a time, the network learns how to adapt tounknown situations

Well suited for non-stationary environments


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Training of NN1

Obstacle free simulationenvironment

The vehicle moves along

different paths around thetarget, traversing differentvehicle-target orientations andpositions

For each vehicle targetconfiguration, a desired positionclass Tiis assigned for errorback propagation


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Training of NN2

Given a fixed vehicle targetconfiguration, variousobstacles are placed in the

environment Each training sample is

assigned one of the 30possible obstacle

configuration classes


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Backpropagation in NN1 and NN2

Initialize weights

Apply input vector X

Compute outputs Yofhidden layer

Compute outputs Cofoutput layer

Compute error:

Update weights:

Repeat until error


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Training of NN3

Reinforcement learning by trial-and-error

Penalty rewardsystem is used

Target localization penalty: a human criticpenalizes target localization

Obstacle avoidance penalty: sensor datadetermines the distance to the closest object in

each direction Plocalization


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Simulation results

Neural network approach providesrobustness

Real time performance, for simple as well ascomplex environments

Static, intelligent and non-intelligent movingobstacles are successfully avoided


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Static environments


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Intelligent obstacles


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Non-intelligent obstacles


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Complex environments


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Conclusions

Neural network approach to motion planning

Advantages

Powerful adaptive learning: real-time performance in

unknown environments Ease of use: little domain-specific expertise needed

Built-in robustness: due to redundancy, the networkcapabilities may be retained with network damage

Disadvantages Dead-end situations are hard to avoid


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Hybrid methods

Memory elements allow for long-termbehavioral patterns

E.g. avoidance by reversing path or stopping briefly

Output a setof actions: helps avoiding dead-end situations (i.e. local minima)

Use of genetic algorithms deduce the optimal

network structure Improves learning performance

Disadvantage: requires domain specific expertise,which is not needed for hierarchical structure


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