Contribution to the Control of Mobile Systems using the ......Contribution to the Control of Mobile...

Contribution to the Control of Mobile Systems using thePrinciple of Bio-inspired Adaptive Autonomy

Thomas GlotzbachIlmenau University of Technology

Presentation in the framework of theInternational Graduate School on Mobile CommunicationsIlmenau, 21.01.2010

21.01.2010 www.tu-ilmenau.deThomas Glotzbach

Information about the speakerThomas Glotzbach, born 1976 in Hünfeld

2001 Graduation at University of Applied Sciences Fulda

2001-2010 Occupation as Researcher at Ilmenau University of Technology, Institute for Automation and System Engineering (IUT) and/or the Fraunhofer Center for Applied Systems Technology Ilmenau (AST)

2005 Successful conclusion of Promotionseignungsfeststellungsverfahren at IUT

2010 Granting of doctorate at IUT

2010-2011 Occupation as Post-doc Researcher at Technical University of Lisbon, Instituto Superior Técnico, in the framework of a Marie Curie Fellowship of the European Commission


Content1. Motivation / Problem / Task

2. Concept of the Bio-inspired Adaptive Autonomy

3. Undertaken Implementations of mobile system teamsHybrid approachSwarm behaviour of biological and technical systemsAnalytical Modelling of a vehicle team as ‘ liquid droplet’

4. Recapitulatory assessment of the different team strategies

5. Possible applications

6. First results of practical implementation – the GREX project

7. Recapitulation / Outlook

1. Motivation / Problem / Task








Motivation: Term ‚Autonomy‘Missing general definition of the term ‘Autonomy’ constricts the necessary

interdisciplinary cooperation in the area of mobile systems

Fraunhofer INT:Automation

and Autonomy

Edlinger:Autonomy as SLAM

Pfützenreuter:Autonomy without contact

to human

Schwan (DirBWB):Autonomy in military

applications

Smithers:Autonomy as maintenance

of functionality (homoeostasis)


Problem: Definition of Autonomy for mobile systems

autonomous ⇔ self-dependent, automatic,but:

• How much ‘self-dependent behaviour’ is necessary to call a system ‘autonomous’?

• Can a system be called autonomous if it gets information from outside during the mission?

• Can a human operator intervene into a mission execution without ‘destroying’ the autonomy

• Given a strict usage of the definition of autonomy, is it even possible to realise a ‘cooperating team’ of ‘autonomous systems’?


Focus of my research work during doctorate

• Development of a autonomy concept to solve the shown problems.

• Autonomy concept shall especially solve the problems of the integration of humans and the team creation.

• Simulation of a team of autonomous mobile systems with the task to go to a predefined target position in an area with unknown obstacles and realisation of first team behaviours with real maritime vehicles in reality.


Concept of the Bio-inspired AdaptiveAutonomy

• Mobile Robotik: neither (total) autonomous nor remote controlled solutions are suggested.

• It is NOT: The more autonomous, the better

• BUT (NEW APPROACH): Level of Autonomy is variable and dependent on task and situation.

Mobile Systems 1 – 3 D

Level of Autonomy

remote controlled

(total)-autonomous

Situation TaskAdapter

35 65


Integration of human(Total) autonomous system

LocalIntelligence,

sensors

Semi-auto-nomoussystem

LocalIntelligence,

sensors


LocalIntelligence,

sensors


Mission area (dangerous)Range of command (secure)

Central Control

(Artificial Team-intelligence)

Human

Human

Information

Commands


Integration of human(Total) autonomous system

LocalIntelligence,

sensors


LocalIntelligence,

sensors


LocalIntelligence,

sensors


Mission area (dangerous)Range of command (secure)

Central Control

(Artificial Team-intelligence)

Human

Human

Information

CommandsDifferentiation between GLOBAL and LOCAL

intelligence

Involvement of Human at the autonomous system:

• Human is superior to artificial intelligence in cognitive tasks.

• He has to accept the full ethical and juristic responsibility for all activities. Therefore, several activities must be activated directly by him.

• He is located in the secured area.


Bio-inspired Adaptive Autonomy regarding a control theory aspect

highly autonomous system low- (non-) autonomous system


Implementation of the adapter for the autonomy level 1/2

Value-continuous implementation of the autonomy adapter

Implementation as Fuzzy system


Value-discrete implementation of the autonomy adapter

Level

Description Relevance

0 Tele-operated Operator performs tele-operating

1 Semi-autonomous Operator forces new sub-ordinatetargets; system executes them

2 Autonomous System executes pre-definedmission plan

3 Obstacleavoidance

System discontinuous executionof mission plan to pass an obsatcle

4 Loss of communication

System returns towards base to reestablish a communication link

Implementation of the adapter for the autonomy level 1/2


Reasons to use teams of autonomous systems

Reasons to employ teams of mobile systems:Obtainment of System synergies = Emergence

i.e.: Team with n systems, A: Ability

1i

n

Team Systemi

A A=

> ∑


Team

Bio-inspired Adaptive Autonomy in teams of mobile systems

mobile system 1

remote controlled

autonomous

Situation TaskAdapterFormation

mobile system 2

remote controlled

autonomous


Teaminstanceremote controlled

autonomous


mobile system 3

remote controlled

autonomous


Communication


Summary of undertaken implementations of mobile system teams


Summary of undertaken implementations of mobile system teams

Supervised collegiate works at IUT (selection):Brückner, I.: Simulation von Bewegung und Steuerung des autonomen mobilenRobotersystems MauSI 2 in virtuellen Räumen. Diploma thesis, 2002

Frühling, R.: Erweiterung und Verbesserung der Infrarotsensorumsetzung imSimualationsmodell des autonomen Robotersystems „MauSI 2“. Project thesis, 2005.

Glotzbach, T.; Kopfstedt, T.; Wernstedt, J.: Simulation von Rudelverhaltenautonomer Systeme. Research report, 2003, unpublished.

Supervised collegiate works at IUT (selection):Grahle, T.: Kooperierende Zielsuche und Umgebungserkennung in dynamischen Umgebungen. Diploma thesis, 2004

Holzapfel, S.: Missionsmanagement eines großenautonomen Roboterschwarmes durchEvolutionsstrategien. Diploma thesis, 2004

Implementation according to:Elkaim, G.; Siegel, M.: ‘Lightweight Control Methodology for Formation Control of Vehicle Swarms.’ In: Proceedings of the 16th IFAC World Congress, Prague, Czech Republic , July 2005.


Hybrid approach – Modelling of vehicle and environment

Mobile Robots ‚MauSI‘Modell eines autonomen Systems

variabler Intelligenz

Demonstrator





Demonstrator

Translative Movement:

Rotatory Movement:





Demonstrator

Translative Movement:

Rotatory Movement:

Model of the infrared sensors:Linear equation of sensor line through x0,y0 with angle α

Linear equation of obstacle edge between x1,y1 and x2,y2

Calculation of intersection between the two lines

Calculation of distance between intersection and sensor

( ) ( )2 20 0s ss x x y y= − + −


Rule-based control of a single systemImplementation of target approach and target shift by two fuzzy systems

Example:

Input and output of the fuzzy system for target approach

Input: Distance

Target Distance / cm

Mem

bers

hip

Val

ue

Mem

bers

hip

Val

ue

Output: Left_v, Right_v

Set point values of engine speed (left, right) / (10-2)*(m/s)


Rule-based control of a single system

Set point values of engine speed

Control systemfor target approach

Inputs: position of target (angle, distance)

Outputs: set point values of engine speed

Level 1 - Control systemfor target approach

Inputs: position of target (angle, distance)

Outputs: set point values of engine speed

position of the mobile system

Calculation oftarget position

Mission plan

Current target

IR-sensors

Level 2 – Control systemfor obstacle avoiding(by target shifting)

Inputs: sensor values, position of real target(angle, distance)

Output: Virtual target


Expansion of the concept for three systems


Mission plan

Current target

Position,

IR-sensors

Virtual Target Level 2

Level 3 - Control systemfor team intelligence

Level 2 – Control systemfor obstacle avoiding

Level 1 - Control systemfor target approach Set point values of

engine speed

Virtual Target Level 1

Expansion of the concept for three systems- the single system’s point of view


Implementation of the Level 3 – Control System as state graph

This state graph realises the Adaptive Autonomy

Direct Target Approach

Formation Building

Obstacle Avoidancerobot with raised

Level of Autonomy

Mission Start

Mission End

Descr. Incident

A Formation OK

B Formation not OK

C Obstacle in target direction

D No obstacle in target direction

E Target approached

AA

A

B

BB

C

C

CD

E

E

E


Implementation of the Lv. 3 – Control SystemPresentation in MATLAB/Simulink©


Example mission of a robot team

• Target Approach for three vehicles in a row formation

• Arrangement of obstacles is not known in advance

• Obstacle avoidance in line formation

• Thick, coloured pins: Target of 1. Level (real or virtual Target Level 1)

• Thin, golden pins: virtual Target Level 2


AutopilotManoeuvre managementMission management

Human

Central Computer (CC)

Robots (Red, Green, Blue)

CC (internal) Formation not OKCC to swarm Build formation!

Human to CC "Transfer the robot swarm to the defineddestinations in a row formation!"

Red, Green, Blue New destination received


TECHNISCHEUNIVERSITÄTILMENAU

Autopilot

Mission managementManoeuvre management

Human

Central Computer (CC)

Robots (Red, Green, Blue)

Human to CC "Transfer the robot swarm to the defineddestinations in a line formation!"

Red, Green, Blue "New targets received!"

CC (internal) Formation not OKCC to team "Establish formation!"

Red, Green, Blue "New targets received!"Red to CC "Destination reached!"Red, Green, Blue "New targets received!"Red to CC "Destination reached!"Green to CC "Destination reached!"

Red to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"Red, Green, Blue "New targets received!"

CC (internal) Formation not OKCC to team "Establish Formation!"CC (internal) Formation OKCC to team "Approach Target!"

Blue to CC "Obstacle detected!"Red OK, I guide towards targetGreen OK, I follow RedBlue OK, I follow Green

CC (internal) Formation OKCC to team "Approach Target!"CC (internal) Discontinue formationCC to team "Red leads, the other robots follow!"

Red OK, I guide towards targetGreen OK, I follow RedBlue OK, I follow GreenRed Obstacle detected, avoiding

Green OK, I follow RedBlue OK, I follow GreenRed Obstacle detected, avoidingGreen Obstacle detected, avoiding

Blue OK, I follow GreenRed Obstacle detected, avoidingGreen Obstacle detected, avoidingRed Obstacle passed, old destination

Red Obstacle detected, avoidingGreen Obstacle detected, avoidingRed Obstacle passed, old destinationBlue Obstacle detected, avoiding

Green Obstacle detected, avoidingRed Obstacle passed, old destinationBlue Obstacle detected, avoidingGreen Obstacle passed, old destination

Red Obstacle passed, old destinationBlue Obstacle detected, avoidingGreen Obstacle passed, old destinationBlue Obstacle passed, old destination

CC (internal) Discontinue formationCC to team "Red leads, the other robots follow!"CC (internal) Team has passed obstacleCC to team "Establish formation!"

Blue Obstacle detected, avoidingGrün Obstacle passed, old destinationBlue Obstacle passed, old destinationRed, Green, Blue "New targets received!"

Green Obstacle passed, old destinationBlue Obstacle passed, old destinationRed, Green, Blue "New targets received!"Red to CC "Destination reached!"

Blue Obstacle passed, old destinationRed, Green, Blue "New targets received!"Red to CC "Destination reached!"Green to CC "Destination reached!"

Red, Green, Blue "New targets received!"Red to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"

CC (internal) Team has passed obstacleCC to team "Establish formation!"CC (internal) Formation OKCC to team "Approach Target!"

Red to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"Red, Green, Blue "New targets received!"

CC to team "Establish formation!"CC (internal) Formation OKCC to team "Approach Target!"CC to Human "Mission accomplished!"

Red, Green, Blue "New targets received!"Red to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"

Human to CC "Transfer the robot team to the defineddestinations in a line formation!"

Human OK, I have recognized it!"

Green Obstacle detected, avoidingRed Obstacle passed, old destinationBlue Obstacle detected, avoidingGreen Obstacle passed, old destination


CC (internal) Formation not OKCC to team "Establish formation!"CC (internal) Formation OKCC to team "Approach Target!"CC (internal) Discontinue FormationCC to team "Red guides, the others follow!"CC (internal) Team has passed obstacleCC to team "Establish formation!"CC (internal) Formation OKCC to team Approach Target!CC to Human Mission accomplished!

Red, Green, Blue New targets receivedRed to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"Red, Green, Blue New targets receivedBlue to CC "Obstacle detected!"Red OK, ich guide towards destinationGreen OK, I follow RedBlue OK, I follow GreenRed Obstacle detected, avoiding!Green Obstacle detected, avoiding!Red Obstacle passed, old destination!Blue Obstacle detected, avoiding!Green Obstacle passed, old destination!Blue Obstacle passed, old destination!Red, Green, Blue New targets receivedRed to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"Red, Green, Blue New targets receivedRed to CC "Destination reached!"Green to CC "Destination reached!"Blue to CC "Destination reached!"

Human to CC "Transfer the robot team to the definedHuman OK, I have recognized it!"

Performed activities and communications

=> Relief of the Human

Example mission of a robot team


Swarm behaviour of biological and technical systems• Base idea: team with large number of simply build-up members.

• Prototype: ants or termites

• Emergence is expected to happen here.

• Important: No direct communication possible

• Principle of Stigmergy: data exchange by manipulation of the environment,

e.g. by spraying of pheromone.

• Two examples:

1. Ants swarm, searching for food

2. Transfer of principle for mobile robots in unknown environments


Simulation of ants on search for food

• Hybrid approach with lists and a two-dimensional grid map

• Objects: Ant-hill, obstacles, food (static), search and transport ants

(dynamic)

• Ant-hill and food are surrounded by scent (strength for food is relative to

food quality.

• Simple modelling of ants: They can move a specified length and turn by a

specified angle per simulation step.

• Ants touching food piece takes it, starts to stray pheromone, and lays it

down when reaching the ant-hill.


Simulation of ants on search for foodTouch and smell area of a simulated ant


Scents and pheromonesSimulation of ants on search for food

Field of scent

Produced by food and ant-hill

(additive covering):

( ) ( ) ( )( ) ( )

2 2

, , cos2Food Food Food Food

Food Food

X x Y yx y

Radπ

η κ η κκ

⎛ ⎞⋅ − + −⎜ ⎟= ⋅⎜ ⎟⋅⎝ ⎠

Trace of pheromone

Single vaporising scent fields,

sprayed by ants.

Ant can follow trace by keeping

scent concentration in left and

right smelling area equal.


• Ants can employ different strategies both for the search for food and for the ant-hill.

• Probabilities for the different search strategies can be parameterised.

• Strategies:

– Pheromone tracing

– Dessert run

– Ant tracing

– Lucky search

• Strategies for obstacle avoidance:

– Coincidental evasion

– Sidewise evasion

– Wall tracing



Operation chart for pheromone tracing and sidewise evasion



Transfer of principle for mobile robots in unknown environments

• A large (indefinite) number of robots entering a mission area on the lower left corner.

• They shall move to the upper right corner and leave the area there.

• They cannot communicate with each other.

• After leaving the area, they transfer their used routes to a central control instance (daemon LUTIO).

• The daemon searches for possible shortcuts and creates and edits a probability matrix which tells the robots for each position of the grid map, which direction shall be used with which probability.

• The current matrix is given to all robots entering the matrix OR a new map is sent to all robots on the field.

=> Stigmergy-like approach without pheromones


Transfer of principle for mobile robotsDesign of mission area as directed graph with knots and edges

• Decision matrix contains a virtual pheromone value for all edges

• Robots shall move according the following state graph:

directtarget

approach

walltrace

goal

‘phero-mone’traceTransfer Condition

B1 Aimed knot blocked by obstacle

b2 Obstacle avoided

b3 At least one edge at current knot has a pheromone value grater than zero

b4 No pheromone trace to new knot

b5 Pheromone trace blocked

b6 Current knot is target knot


Transfer of principle for mobile robots• Obstacle avoidance:

– Choose direction (randomly)

– Follow wall

– Exit, when direction towards target knot is free and sum of all turnings is zero

• ‘Pheromone’ tracking:

– Calculate probability value for all edges at current knot according to following equation:

(τ - amount of ‘virtual pheromone’ on edge, η - distance between edge and goal knot)

– Make decision among all edges with values bigger than zero.

( )( )

( ),

, , , ,, ,

, , , ,i j

i j k i j ki j k

i j l i j ll J

tp t

t

α β

α β

τ η

τ η∈

⎡ ⎤ ⎡ ⎤⋅⎣ ⎦ ⎣ ⎦=⎡ ⎤ ⎡ ⎤⋅⎣ ⎦ ⎣ ⎦∑


Add extra pheromones to support search for possible shortcuts

Extra Pheromone for the global best (shortest) route

Extra Pheromone for best (shortest) route of current group

Pheromone for all successful routes of current group

Vaporisation

( ), , , , , , , , , , , ,1

11l

neu l si j k i j k i j k i j k i j k i j kL L L

κ λν κ λ

ν

κ λτ ρ τ σ σ σ τ=

⎛ ⎞= − ⋅ + ⋅ + ⋅ + ⋅ + Δ⎜ ⎟⎝ ⎠

∑

Transfer of principle for mobile robotsCalculation of pheromone amount by daemon LUTIO:

• After a certain amount of robots have found a possible route to the target, the daemon calculates a new ‘pheromone’ matrix (loops within routes are eliminated before):


Transfer of principle for mobile robotsStrategies of the search for possible shortcuts by daemon LUTIO:

• Best of Random Shortcut (BORASH)

• Intelligent Shortcut (INSH)

• Switched Edges (SWIED)

• Blur Pheromone (BLUPH)


Transfer of principle for mobile robotsExample for mission with complex obstacle

Colours of robots:Green – Direct Target Approach

Blue – Wall Trace

Yellow – ‘Pheromone’ Trace

Red - blocked


Analytical Modelling of a vehicle team as ‘ liquid droplet’

• Base idea: holonomic vehicles, free movement in all direction (2D or 3D),e.g. robot system ‘Robotino’ from Festo.

• Modelling of the vehicle as double integrator

∫∫Fx

Fy

x

y


Analytical Modelling of a vehicle team as ‘ liquid droplet’

• Observation from objects in biological systems, e.g. bird swarms or fish schools• Reynolds, C.: Flocks, birds and schools: A distributed behavioral model. Comput.

Graph., 21, 1987, S. 25-34:– Defines three simple rules for the behavior of individuals in large teams, in

relation to their proximate neighbors:• Avoid grouping (separation)• Align direction of movement (alignment)• Avoid breakup (cohesion)

• Proceeding proposed by Elkaim, G.; and Siegel, M.: ‘Lightweight Control Methodology for Formation Control of Vehicle Swarms.’ In: Proceedings of the 16th IFAC World Congress, Prague, Czech Republic , July 2005:

– Treat vehicle team like a liquid droplet, flowing along a sloped area around obstacles

– Introduction of mechanical springs between vehicles, between vehicles and ‘virtual leader’ and between vehicles and obstacles


Obstacle AvoidanceAnalytical Modelling of a vehicle team as ‘ liquid droplet’

• Force of a spring is spring springF K s= ⋅Δ

• Sum force on a vehicle is:

Obstacle

Separation

CohesionVL

Alignment1 2

3 111 +−+ +++= iob

ivl

iij

iij

iS FFFFF

• Spring force is zero when vehicles are in the desired formation

• Team can be moved by moving the virtual leader

• The force between vehicle and obstacle is the quotient of spring constant by distance and is omitted, if a certain distance is exceeded.


Analytical Modelling of a vehicle team as ‘ liquid droplet’• MATLAB examples:

Normal Movement

Collision during formation change

Successful obstacle

avoidance

Obstacle avoidance

fails


Recapitulatory assessment of the different team strategies

Peripheral organisation=> Swarm

Hierarchic organisation=> Pack



Team / Group (umbrella term )• Two or more unmanned vehicles

Pack

• hierarchical realization• few specialists

• mainly analytic / rule- / graph-based control

strategies• Level of Autonomy: low for the single systems, high for

team instance

Swarm

• peripheral realization• many low-level-systems

• mainly evolutionary- / stochastic-based control

strategies• Level of Autonomy: high for the single systems, low

for team instance



Number

Few Specialists

Many Low-Cost-Robots

Boundary of Autonomy

(Total) autonomous Robots

(Total) autonomous system includes human

(Total) autonomous

Swarm

Pack



• Hybrid approach => Pack– Target approach successful– Clear structured, complicated hierarchical control concept– Vehicles in low autonomy level, good cooperation

• Ant simulations => Swarm– Large number of vehicles with low technical requirements, especially no

need for direct communication– Vehicle remain in high autonomy level, only limited cooperation– Rely on evolutionary / stochastic control concept: problematic in real

application.• Liquid Droplet => between Pack and Swarm

– Clear structured, simple control structure– Requirement for holoniomic vehicles: problematic in reality– Problem when used in unknown environments


Cooperating behaviour of participants in road traffic

• Goal: Presentation of a use in road traffic as concept

• Coordination on a two-lane-street (without lanes for opposing traffic)

• Vehicles on both lanes of the street

• Velocity of vehicles: normalised, values without units of 100 on the right and 150 on the left lane



• Obstacle on right lane: Vehicles have to change the lanes.

• Vehicles on right lane with a maximum velocity of 100.

• Vehicles on left lane have to reduce their velocity.


Coloured balls above vehicles = Level of Autonomy:

Green: 100%Red: limited

Black lines between balls = communication

from: Glotzbach, T.: Ein Beitrag zur Entwicklung von Strategien zur Missions- und / oder Manöverführungmobiler Systeme in Schwärmen. 40. Regelungstechnisches Kolloquium, Boppard, Februar 2006.



Example missions of a team of autonomous marine vehicles• Display of possible autonomy levels for marine vehicles on a mission

• Execution of a predefined path in close formation

• Level of Autonomy for single systems and team instance are shown

• Examples are from the EU research project GREX

© Instituto Superior Tecnico, Lisbon© Atlas Elektronik, Bremen

Seawulf DelfimX Infante

21.01.2010 www.tu-ilmenau.deThomas Glotzbach Le

vel o

f Aut

onom

y

(total)autonomous

remoteControlled

Autonomous Unterwater Vehicles (AUVs),Single systems and team instance

Control softw.incl. Adapter(state graph,Fuzzy, etc.)

Sensorinformation

Missionplan

70%/30%


Results of the European Research Project ‚GREX‘

Examples from: “GREX: Coordination and control of cooperating heterogeneous unmanned systems in uncertain environments,” EU Specific targeted research project, IST call 5 by Atlas Elektronik GmbH, Ifremer, Innova S.p.A, MC Marketing Consulting, SeeByte Ltd., IlmenauUniversity of Technology, Instituto Superior Tecnico – IST/ISR, IMAR-DOP/University of the Azores from 2006 - 2009

Marine Habitat MappingQuest for hydrothermal vents


Project ‚GREX‘ – an overview

• Development of a User Interface for multiple vehicle mission preparation, programming, and post mission analysis,

• Generic control system for multiple marine vehicle cooperation taking into account mission alteration and event triggered actions on the fly,

• A cooperative navigation solution for relative positioning,

• A generic multichannel communication system(LAN, radio, and underwater acoustic),

• Realisation and validation by sea trials.


Project ‚GREX‘ – an overview

Cooperative Navigation

Multi Vehicles Mission PlanSingle Vehicle Mission Plan

Team Handler /Coordinated Control

GREX InterfaceModule

proprietary controlsystem

Communication/Network

GREX IFPlugin


Project ‚GREX‘ – Results of final Sea Trials

26. October – 06. November 2009 in Sesimbra, Portugal

Pictures © The GREX Consortium 2009



• Coordination software for GREX vehicle was developed by Ilmenau

University of Technology in close cooperation with Instituto Superior Tecnico

(Lisbon, Portugal)

• Principle: Different team behaviour were defined and realised with so called

‚Multi Vehicle Primitives (MVPs)‘

• Different strategies and team structures for different MVPs

• Realisation of the concept of Adaptive Autonomy



MVP: CPF (Coordinated Path Following) – Moving along predefined paths and

maintain close formation – equal vehicles, no leader


Project ‚GREX‘ – Results of final Sea TrialsMVP: CPF (Coordinated Path Following) – Moving along predefined paths and

maintain close formation – equal vehicles, no leader


Project ‚GREX‘ – Results of final Sea TrialsMVP: CLOSTT (Coordinated Line of Side Target Tracking) – Tracking of a target

by a group of vehicles with a defined leader


Project ‚GREX‘ – Results of final Sea TrialsMVP: CLOSTT (Coordinated Line of Side Target Tracking) – Tracking of a target

by a group of vehicles with a defined leader

Pictures © Instituto Superior Tecnico, Lisbon


Recapitulation 1/2• The concept of the Bio-inspired Adaptive Autonomy (BAA) allows for the

integration and the comparison of different control strategies for mobile systems.

• By using the concept of BAA, it becomes the goal not to realise the maximum, but the best fittest level of autonomy.

• The integration of a human operator as well as the realisation of mobile system teams are describable in the framework of BAA

• The possibility to change the level of autonomy online by an adapter is one of the main performance features of BAA.


Recapitulation 2/2• In the framework of BAA, several implementations of vehicle teams

have been performed and simulated.

• A hybrid approach with analytic, rule and graph based parts was compared with approaches based on stochastic and evolutionary strategies which were influenced by biology.

• The notations ‘pack’ and ‘swarm’ were introduced and compared for the principle realisation possibilities.

• Additional applications, like in public road traffic, have been purposed

• First results achieved in the research project GREX have been presented


Outlook

• Realisation of applications for MUMVswithin European research projects (GREX-2, CONMAR)

• Realisation of adequate system demonstrators; also in areas with low autonomy level (tele-operation, Human-Machine-Interface)

• Improvement of the concept of Bio-inspired Adaptive Autonomy in one more dimension.

• Design of innovative technologies in the area of sensor technology, like localisation for obstacle avoidance, based on fusion of sensor data

• Transfer of technologies from the area to applications in the framework of assistant systems for humans driving vehicles etc.

Continuative research (Ilmenau University of Technology, Fraunhofer AST and author) :

Pictures © Fraunhofer AST Ilmenau

Picture © InstitutoSuperior Tecnico, Lisbon


Further information:

[1] T. Glotzbach: Ein Beitrag zur Steuerung von mobilen Systemen auf Grundlage derBioorientierten Adaptiven Autonomie (Contribution to the Control of Mobile Systems using the Principle of Bio-inspired Adaptive Autonomy). Dissertation Thesis of the Faculty of Computer Science and Automation of the Ilmenau University of Technology, 2010.

[2] Homepage of the GREX-Project:

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