Swarm Intelligence and Bio
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Transcript Swarm Intelligence and Bio
2008 Fall Robotics Class Lecture
Sang Woo Lee
Biologically Inspired Algorithm
Swarm Intelligence
Evolutionary computation
Application in Robotics
Trail-Laying Robots for Robust Terrain
Coverage
Dynamic Redistribution of a Swarm of
Robots
Biomimetic Visual Sensing for flight control
Simulate biological phenomena or model
Working algorithm in nature
Proven its efficiency and robustness by
natural selection
Dealing too complex problems
Incapable to solve by human proposed
solution
Absence of complete mathematical model
Existing of similar problem in nature
Adaptation
Self-organization
Communication
Optimization
Robotics
Multi-Robot Motion Planning
Self-configuration
Network
Distributed autonomous system
Routing algorithm
Social Organization
Traffic control
Urban planning
Computer Immunology
Population of simple agents
Decentralized
Self-Organized
No or local communication
Emergent behaviors
Example
Ant/Bee colonies
Bird flocking
Fish schooling
Meta-heuristic Optimization
Inspired from the behavior of ant
colonies
Shortest paths between the nest and a
food source
Applied problems
Traveling Salesman Problem
Quadratic Assignment Problem
Job Shop Scheduling
Vehicle and Network Routing
Evaporating pheromone trail
Probabilistic path decision
Biased by the amount of pheromone
Converge to shortest path
Ant trips on shorter path returns quicker
Longer path lose pheromone by evaporating
J. Svennebring and S. Koenig, “Traillaying robots. for robust terrain
coverage,”, Proc. of IEEE International
Conference on Robotics and Automation
2003, Volume: 1, On page(s): 75- 82 vol.
1
Inspired by Ant forage
Exploration & Coverage
Pebbles III robot
6 infrared IR proximeter
Front, front-left, front-right, left, right, and rear
Bump sensor, 2 motors
Lay trails – Black pen to track trail (C)
8 Trail sensors(A, B)
Each side 4 sensors
Node Counting
Robot repeated enter cells
Counting by markers in cell
Move to smallest number
No communication
Very limited sensing
Very limited computing power
Marking current cell
Sensing markers of neighbor cells
Assumptions on theoretical foundation
Move discrete step
Mark cell uniformly
No noise in sensor
By the way, it works even
Uneven quality trail
Some missing trail
Pushed to other location
Obstacle Avoidance Behavior
Inversely proportional to the distance
Weight for each direction sensor
Trail Avoidance Behavior
Fixed length
Trail sensor with recent past information
Weight Balancing of two behavior
Need to well balanced
Work well with
Uneven quality trail
Move another location
Removing patches of trail
Faster than random walk
Until some threshold
Too many trails result large coverage time
With no cleaning of Trail
Coverage time grow steeply
With cleaning of Trail
Same as ant pheromone
Works good with many coverage number
Ant robot video
A. Halasz, M. Ani Hsieh, S. Berman, V. Ku
mar. Dynamic Redistribution of a Swarm
of Robots Among Multiple Sites, 2007 IE
EE/RSJ International Conference on Intelli
gent Robots and Systems.
Inspired by Ant house-hunting
Probability of initiating recruitment depends
on the site’s quality
Superior site scout has shorter latency to
recruit
Recruitment type
Summon fellow by tandem run
Passive majority by transport
Transport recruitment of new site triggered
by population (Over the quorum)
Recruitment speed difference amplified by
quorum requirement
Collectively distributes itself to multiple
sites
Predefined proportion
No inter-agent communication
Similar to task/resource allocation
Scalable
Using fraction rather than agent number
Graph G
Strongly connected graph
Edge
Transition rate Kij
Transition time Tij
Maximum transition capacity
All agents know Graph G
Property
Stability
Convergence
To a unique stable equilibrium point
Proved analystically
Transition in equilibrium state
Fast transition makes more idle trips
Extension
Inject Quorum sensing
Fast converge, less idle transition
Adjacent sites communication
Quorum information instantly available
Transition rate switch
Above quorum to below quorum
Set to maximum transition rate
Stable
Converges asymptotically
Problem
Increasing quorum increase convergence
speed
Too big quorum make system stuck by high
transition rates
Bio-inspired sensors and algorithms
UAV
Insects use Optic flow
Perception of depth
Large compound eyes
Perception of the horizon
Ocelli optical sensor
Unmmaned Aerial Vehicle
Aircraft with no pilot
Remotely controlled
Fly autonomously
Usage – Dangerous situations
Military Surveillance
Civil Application
Optic flow
Used for autonomous navigation
Complex Environment
Conventional Aircraft Sensors
Inertial Measurement Unit
Gyro – angular acceleration
Accelerometer – linear acceleration
Global Positioning System
Pressure sensor
Radar for range finding
Suitable for large airplane
Appliable even for microsize UAV(~15cm)
Weakness
Electronic jamming
Low flight altitude
Several meters
Complex environment
Need to know all geometric information
Movement of texture
Resulting from the insect’s motion
Same as Image velocity
Flight Altitude
Observed by downward direction
Low when fast optic flow
Obstacle detection
Expansion – divergence in forward direction
Close when rapid optic flow
Origin of optic flow
Indicate direction of heading
Inside of rapidly expanding region
Imminent collision
Outside of rapidly expanding region
Near obstacle
No collision
Top view of UAV
OF = – ω + (v/d) sin θ
ω – due to self rotation
Right term
Due to translation relative to the obstacle
Θ=0
Move toward obstacle
Only due to self rotation
Trade off of looking forward
Quick detection of a bump or obstacle
Reduces the magnitude of the optic flow
due to the obstacle
Can detect self-rotation
Can replace gyros
Insect behaviors using optic flow
Centering Response
Landing Strategy
Saccade response
Hovering Strategy
Clutter response
Forward focus of expansion strategy
Fixation strategy
Forward collision response
Centering Response
Equalize the optic flow on the left and right
sides
Enable to fly center of a tunnel
Landing Strategy
Keep the optic flow on the landing surface
constant
Keep the forward speed proportional to the
vertical speed
Saccade response
Turning away from regions of high optic
flow
Avoid collisions with large obstacles
Hovering Strategy
Zero optic flow everywhere
Useful for docking and for flying in
formation
Clutter response
Maintain average global optic flow constant
To regulate flight speed at a safe level
Useful for dense obstacle environment
Forward focus of expansion strategy
Holding optic flow in forward direction at
zero
To maintain a straight-ahead course
Useful when winds cause sideslip
Fixation strategy
Fixating objects in the forward direction
Minimizing lateral optic flow in the
downward field
To maintain a straight-ahead course
Forward collision response
Measure relative rate of expansion to detect
imminent head-on collisions
D/V – first order approximation
Strategy to decide between saccade
response and forward collision response
Region of high optic flow
Unbalanced – saccade response is enough to
avoid collision
Balanced – prepare to land
Insect Vision
Large compound eyes
Optic flow
Target recognition
Oceli
Top of insect’s head
Low resolution – few pixels
Respond to the horizon for flight stabilization
Halteres
Small structures protruding from the thorax
Vibrate out of phase with the wings
Detect high speed self-rotations
Detect perturbations resulting from rotation
Srinivasan and Chahl
A least-squares-based
optic flow algorithm
Optic flow sensor
Ground robot
experiment
450MHz Pentium III
computer
Fuse optic flow with
gyro information
Dr. Geoffrey Barrows
Develop optic flow
sensor
Can be used for altitude
control
Based on vision chips
Consist of hundred
Elementary Motion
Detector
Weigh only several
grams
Chahl and Stange at ANU
Based on the ocelli of the dragonfly
For horizontal detection
Omnidirectional visual system
To simulate Biological system
Artificial ones
Using curved mirror
Australian National University
RC gas-powered helicopter
1.5m blade spin
Forward speed 50km/h at 10meters altitude
Vision based hover at 40km/h wind
Ocelli
Four directional
60km/h terrain following
Centeye
RC-type fixed-wing aircraft about 1 meter
Optic sensor – 10~20 grams
Altitude control – 2~3 meters to 10 meters
Terrain following (10 degree range)
Collision detection and avoidance
Still working
UC Berkeley
2cm wide-span insect robot
Studying wing-flapping motion
Bio-inspired algorithms has many
application
Simple algorithm becomes emergent logic
Useful for too complex problems
Very useful for swarm of robot control
Simple computation
Decentralized and self-organized
Need no or local communication
Useful to establish group behavior
de Castro, Leandro N. Recent Developments in Biologically Inspire
d Computing, Hershey, PA, USA: Idea Group Publishing, 2004. p vi
i.
Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Opti
mal traffic organization in ants under crowded conditions. Nature
428, 70-73 (2004)
J. Svennebring and S. Koenig, “Trail-laying robots. for robust
terrain coverage,”, Proc. of IEEE International Conference on
Robotics and Automation 2003, Volume: 1, On page(s): 75- 82 vo
l.1
A. Halasz, M. Ani Hsieh, S. Berman, V. Kumar. Dynamic Redistributi
on of a Swarm of Robots Among Multiple Sites, 2007 IEEE/RSJ Int
ernational Conference on Intelligent Robots and Systems.
Barrows, G.L., Chahl, J.S. and Srinivasan, M.V., Biomimetic visual se
nsing and flight control. Aeronautical Journal. v107 i1069. 159-168
.