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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 Page 2
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 3
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
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 4
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)
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 5
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’?
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 6
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.
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 7
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 8
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 9
Integration of human(Total) autonomous system
LocalIntelligence,
sensors
Semi-auto-nomoussystem
LocalIntelligence,
sensors
Semi-auto-nomoussystem
LocalIntelligence,
sensors
Semi-auto-nomoussystem
Mission area (dangerous)Range of command (secure)
Central Control
(Artificial Team-intelligence)
Human
Human
Information
Commands
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 10
Integration of human(Total) autonomous system
LocalIntelligence,
sensors
Semi-auto-nomoussystem
LocalIntelligence,
sensors
Semi-auto-nomoussystem
LocalIntelligence,
sensors
Semi-auto-nomoussystem
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.
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 11
Bio-inspired Adaptive Autonomy regarding a control theory aspect
highly autonomous system low- (non-) autonomous system
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 12
Implementation of the adapter for the autonomy level 1/2
Value-continuous implementation of the autonomy adapter
Implementation as Fuzzy system
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 13
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 14
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=
> ∑
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 15
Team
Bio-inspired Adaptive Autonomy in teams of mobile systems
mobile system 1
remote controlled
autonomous
Situation TaskAdapterFormation
mobile system 2
remote controlled
autonomous
Situation TaskAdapterFormation
Teaminstanceremote controlled
autonomous
Situation TaskAdapterFormation
mobile system 3
remote controlled
autonomous
Situation TaskAdapterFormation
Communication
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 16
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 17
Summary of undertaken implementations of mobile system teams
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 18
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.
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 19
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 20
Hybrid approach – Modelling of vehicle and environment
Mobile Robots ‚MauSI‘Modell eines autonomen Systems
variabler Intelligenz
Demonstrator
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 21
Hybrid approach – Modelling of vehicle and environment
Mobile Robots ‚MauSI‘Modell eines autonomen Systems
variabler Intelligenz
Demonstrator
Translative Movement:
Rotatory Movement:
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 22
Hybrid approach – Modelling of vehicle and environment
Mobile Robots ‚MauSI‘Modell eines autonomen Systems
variabler Intelligenz
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= − + −
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 23
Hybrid approach – Modelling of vehicle and environment
Mobile Robots ‚MauSI‘Modell eines autonomen Systems
variabler Intelligenz
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= − + −
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 24
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)
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 25
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 26
Expansion of the concept for three systems
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 27
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 28
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 29
Implementation of the Lv. 3 – Control SystemPresentation in MATLAB/Simulink©
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 30
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 31
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 32
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 33
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 34
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 35
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 36
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.
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 37
Simulation of ants on search for foodTouch and smell area of a simulated ant
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 38
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.
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 39
• 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
Simulation of ants on search for food
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 40
Operation chart for pheromone tracing and sidewise evasion
Simulation of ants on search for food
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Simulation of ants on search for food
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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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 43
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
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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∈
⎡ ⎤ ⎡ ⎤⋅⎣ ⎦ ⎣ ⎦=⎡ ⎤ ⎡ ⎤⋅⎣ ⎦ ⎣ ⎦∑
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 45
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):
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 46
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)
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 47
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)
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 48
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 49
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 50
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
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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
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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.
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Analytical Modelling of a vehicle team as ‘ liquid droplet’• MATLAB examples:
Normal Movement
Collision during formation change
Successful obstacle
avoidance
Obstacle avoidance
fails
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 54
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
21.01.2010 www.tu-ilmenau.deThomas Glotzbach Page 55
Recapitulatory assessment of the different team strategies
Peripheral organisation=> Swarm
Hierarchic organisation=> Pack
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Recapitulatory assessment of the different team strategies
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
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Recapitulatory assessment of the different team strategies
Number
Few Specialists
Many Low-Cost-Robots
Boundary of Autonomy
(Total) autonomous Robots
(Total) autonomous system includes human
(Total) autonomous
Swarm
Pack
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Recapitulatory assessment of the different team strategies
• 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
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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
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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
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Cooperating behaviour of participants in road traffic
• 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.
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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.
Cooperating behaviour of participants in road traffic
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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
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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%
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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
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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
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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.
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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
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Project ‚GREX‘ – Results of final Sea Trials
26. October – 06. November 2009 in Sesimbra, Portugal
Pictures © The GREX Consortium 2009
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Project ‚GREX‘ – Results of final Sea Trials
Pictures © The GREX Consortium 2009
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Project ‚GREX‘ – Results of final Sea Trials
• 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
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Project ‚GREX‘ – Results of final Sea Trials
MVP: CPF (Coordinated Path Following) – Moving along predefined paths and
maintain close formation – equal vehicles, no leader
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Project ‚GREX‘ – Results of final Sea TrialsMVP: CPF (Coordinated Path Following) – Moving along predefined paths and
maintain close formation – equal vehicles, no leader
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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
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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
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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
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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
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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.
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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
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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
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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:
Thank you for your attention