ER_Chap04

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Robot Intelligence Technology Lab.

Evolution of simple navigation

Chapter 4 of Evolutionary Robotics

Jan. 12, 2007

YongDuk Kim

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2Robot Intelligence Technology Lab.

Contents

Introduction Straight motion with obstacle avoidance

Visually guided navigation

Re-adaptation Cross platform adaptation

From simulation to reality

Conclusions

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1. Introduction

In this chapter, evolution of simple behaviors will be described.

Navigation ability

The development of a suitable mapping from sensory information to

motor actions.

The closed feedback loop (between sensory information and motor

actions) makes it rather difficult to design a stable control system for

realistic situations. One solution

Listing all possible sensory situations and associate them to a set of

predefined motor actions.

The solution is not always viable because of unknown and unpredictable

environments.

Artificial evolution

Smart controller by exploiting interactions between the robot and the

environment.

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2. Straight motion with obstacle avoidance

Braitenbergs vehicle [Braitenberg 1984]

The robot morphology is symmetrical and it has two

wheels.

It is conceptual robot whose wheels are directly linked

to the sensors through weighted connections.

Hand designed solution

It should be noticed that even this simple design

requires careful analysis of sensor and motor

profiles, and important decisions for what concerns

the direction of motion, its straightness, and its

velocity.

Different robots and different environments require

different set of carefully chosen values.

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Evolutionary approach [Floreano and Mondada 1994]

It could find a solution for straight navigation and obstacle avoidancewithout assuming all the prior knowledge about sensors, motors, andenvironment.

The goal was to evolve a control system capable of maximizing forwardmotion while accurately avoiding all obstacles on its way.

The fitness function

Where V is the sum of rotation speed of the two wheels, v is the absolutevalue of the algebraic difference between the signed speed values of thewheels, i is the normalized activation value of the infrared sensor with thehighest activity.

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These three components encourage respectively motion, straight

displacement, and obstacle avoidance, but do not say in what

direction the robot should move.

The control system

A neural network

One layer of synaptic weights from the eight infrared sensors to two motors

units.

The results

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For each generation, it took approximately 40 minutes.

Although the fitness indicators keep growing for 100 generations,

around the 50th generation the best individuals already exhibited a

smooth navigation around the maze.

A fitness value of 1.0 could have been achieve only by a robot moving

straight at maximum speed in an open space.

In the experiments, 0.3 was the maximum value attained by the

evolutionary controller even when continued for further 100

generations.

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Values of the fitness components

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3. Visually guided navigation

It is very likely that by the end of the this decade almost all

autonomous robots will employ vision as a primary sensory system.

Mainstream approach to vision processing [Marr 1982]

Based on preprocessing, segmentation, and pattern recognition

Is not viable for systems that must respond very quickly in partially

unpredictable environments.

A drastic new approach

Takes into account the ecological aspects of visual based behavior and

its integration with the motor system.

There were only few efforts into this direction. [Horswill 1993;

Marjanovic et al. 1996]

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Evolutionary robotics provides an ideal framework.

It allows the development of visual processing along with motor

processing in closed feedback loop.

It relies less on externally imposed assumption.

It allows simultaneous exploration of both controllers and sensor

morphologies.

The ecological vision is going to be a very fertile area for evolutionary

robotics over the next years.

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Gantry robot [Harvey 192a, 1992b, 1993]

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The visual input is considerably reduce by sampling only a small part of the

image according to genetically specified instructions.

The neural networks have a fixed number of input and outputs

Artificial evolution was carried out in three stages of increasing behavioral

complexity

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4. Re-adaptation

The price to pay for the automatics process of adaptation described

above is the amount oftime required by evolution carried out entirely

on physical robots.

The question the is to what extent and at what speed an evolutionary

system can generalize and/or re-adapt to modified environmental

conditions without retraining it from scratch.

Cross platform adaptation [Floreano and Mondada 1998]

In some cases, it might be desirable to continue evolution on the new

robot incrementally.

From the point of view of the neurocontroller, changing the sensory motor

characteristics of the robot is just another way of modifying theenvironment.

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4. Re-adaptation

Incremental evolution still requires quite a lot of research in order toaccommodate more complex sensory motor systems, acquisition of

new skills, modification of old ones.

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4. Re-adaptation

From simulation to reality

Simulations can provide a valuable aid to evolutionary robotics as long as

they are coupled with test s on physical robots.

Transferring an evolved controller from the simulated to the real robot is

very likely to generate discrepancies in the behavior and performance of

the robot caused by different properties of the sensory motor interactions

between the robot and its environment.

Experiments [Miglino et al., 1995]

No-noise condition: not including any noise

Noise condition: adding uniform white noise to the simulated sensors.

Conservative-noise condition: the sensory values were read as if the robot had

been displaced by a small random quantity along the x and y coordinates.

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4. Re-adaptation

Environments

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5. Conclusions

Some examples of artificial evolution applied to simple navigation

tasks is presented.

The results indicate that artificial evolution can be fruitfully applied

even to tasks where a preprogrammed strategy already exists or to

tasks that are apparently simple.

Even slight modifications to the environment of an evolved individual

are likely to cause a drop in performance.

Performance can be rapidly recovered.

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