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
.