... ... ... ... Physical Design of the Segments Time in arbitrary units Phase (+display offset) CPG number Small, lightweight design Strong enough to lift themselves Modular design 3-wire “spinal cord” On-board power supply (rechargeable battery) True local control (CPG algorithm on segment) torque, temperature, battery oltage, Local sensory input: rotary position, motor v 3 x ambient light Reconfigurable inter-segmental connections (0 or 90 degrees) A specialized head segment: - wireless communication with PC - report and modify states of all other segments - reprogram CPG-algorithms during operation A CPG-driven Autonomous Robot Introduction We have built an autonomous mobile robot which facilitates exploring motion principles based on neural Central Pattern Generator (CPG) circuits in a truly distributed system. The main aim of the project is to demonstrate elegant motion on a robot with a large number of degrees of freedom under the control of a simple distributed neural system as found in many animals' spinal cord. Currently, the robot consists of up to 60 individual segments that all run a local CPG. Sparse adjustable short- and long-range coupling between these CPGs synchronizes all segments, thus generating overall stable motion. A wireless connection between a host computer and the robot allows changing parameters during operation (eg. individual coupling coefficients, traveling speed, and motion amplitude). Additionally, users can modify the CPG algorithm and reprogram the segments during operation. The robot can demonstrate various motion patterns based on extremely simple neural algorithms. We are currently implementing more advanced neural CPGs and will compare them in terms of motion robustness and traveling speed. This poster presents the neural inspiration behind the robot (2, 3) as well as its physical design (4), discusses some experimental results in locomotion (5-7), and suggests some open questions for future research (8). Jörg Conradt, Rodney J. Douglas Paulina Varshavskaya Kerstin Preuschoff , Institute of Neuroinformatics, ETH / University Zurich , CSAIL, Massachusetts Institute of Technology , CNS, California Institute of Technology No paired fins sticking out One wavelength / body length (of roughly 100 segments) Other examples for CPG-driven motion: Eels, Snakes Oscillation frequency of body proportional to traveling speed Distributed CPG segments along the spinal cord Left and right alternating activity, traveling uniformly down the body Intersegmental coupling to generate stable traveling wave Pictures of lampreys Activity of CPGs in a 50 segment isolated piece of spinal cord A Mathematical Model for CPG-driven Locomotion Data and Image courtesy of Avis Cohen, University of Maryland Frontal view of a segment Sketch of the robot in side-view 1 Funded by the and by travel grants from the Institute of Neuroinformatics, ETH / University of Zurich Neuromorphic Engineering’s Research Collaborative Network (INE-RCN) Experiments Results 3 5 6 4 The Lamprey, a Simple Example for CPG-driven Locomotion 2 Discussion Future Directions Integrating local sensor signals Implementing more detailed CPG algorithms Adding an accelerometer, using signals to learn parameters for efficient forward motion Stabilizing the head during motion (accelerometer) Adding vision, processing data onboard (eg. simple color blob tracking), goal directed motion Finding sparse coupling network Commercializing the robot: (adapting local CPG behavior to external stimuli) targeting academia or the toy-market? 8 Cohen, A.H., Holmes, P.J., Rand, R.H. (1982). The Nature of Dowling, K.J. (1997). Limbless Locomotion: Learning to Crawl Conradt, J.,Varshavskaya, P. (2003). Distributed Central Pattern he Couplings B Generator Control t etween Segmental Oscillators of the Lamprey Locomotion: A Mathematical Model, in Vol.13, pp.345-369. with a Snake Robot, PhD Thesis, CMU for a Serpentine Robot. Joint International Conference on Artificial Neural Networks a Neural Information Processing (ICANN/ Istanbul, Turkey. Spinal Generator for Journal of Mathematical Biology, nd ICONIP), References: 7 ! ! ! ! Robot motion in different mechanical configurations: - planar horizontal robot (snake-like ) - planar vertical robot (worm-like) - alternating horizontal and vertical rotating segments (motion in 3D) Parameters for generating motion: - frequency of oscillation in segments - amplitude of oscillation in segments - phase offset between neighboring segments - coupling coefficients between segments Motion experiments in different environments: - on table with fixated head and tail-segments - on different surfaces, eg carpet, plastic, wood, stone Data collection: - hand-recorded overall velocity (forward and sidewinding) - sequences of desired and true angular positions Large number of segments, high number of degrees-of-freedom, inherently scalable Robust and failure tolerant system Elegant control by an incredibly simple model Highly complex motion patterns relative to algorithmic cost Different mechanical configurations possible We do not suggest that the robot is a good approximation to a biological system. Rather, we hope that the distributed nature of the robot’s control will allow for interesting experiments pertaining to the function of the spinal cord or other, possibly artificial, neural system. Further documentation and demonstration videos of the robot are available on the web at: http://www.ini.ethz.ch/~conradt/projects/WormBot Current phase of the i CPG oscillator th Frequency of the i oscillator th Transform of instantaneous internal phase to external bending angle Change of oscillator phase, depending on , coupling coefficients along the spinal cord, phase offset between neighboring segments 32 oscillating CPGs, with and without synchronization. Synchronization is enabled ( ) at (red line), when the segments start forming a traveling wave. t=0 i ù i ù ç ij á () max sin i i ö ö è = 0 ij á = ( ) ( ) 1 1 sin N i á i ij j i j dè ù á è è ç i j dt << = = + - + - å ∑ i è [ [ ∈ Simple CPG algorithm sufficient for generating various motion patterns: - sliding motion - side-winding motion - self-lifting traveling wave in planar and alternating configurations Parameter settings for efficient motion depend on the robot’s configuration and on surface properties. Determining parameters empirically is extremely time demanding Variations in the coupling coefficients have effect on motion (above a critical threshold for synchronization) these no 5 cm
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Physical Design of the Segments
Time in arbitrary units
Phas
e(+
dis
pla
yof f
set)
CPG number
Small, lightweight designStrong enough to lift themselvesModular design3-wire “spinal cord”On-board power supply(rechargeable battery)
True local control (CPGalgorithm on segment)
torque, temperature, batteryoltage,
Local sensory input:rotary position, motor
v 3 x ambient lightReconfigurable inter-segmentalconnections (0 or 90 degrees)
A specialized head segment:- wireless communication with PC- report and modify states of all
other segments- reprogram CPG-algorithms
during operation
��������� ������������ �������
A CPG-driven Autonomous Robot
Introduction
We have built an autonomous mobile robot which facilitatesexploring motion principles based on neural Central PatternGenerator (CPG) circuits in a truly distributed system.The main aim of the project is to demonstrate elegant motion on arobot with a large number of degrees of freedom under the control ofa simple distributed neural system as found in many animals' spinalcord. Currently, the robot consists of up to 60 individual segmentsthat all run a local CPG. Sparse adjustable short- and long-rangecoupling between these CPGs synchronizes all segments, thusgenerating overall stable motion. A wireless connection between ahost computer and the robot allows changing parameters duringoperation (eg. individual coupling coefficients, traveling speed, andmotion amplitude). Additionally, users can modify the CPGalgorithm and reprogram the segments during operation.The robot can demonstrate various motion patterns based onextremely simple neural algorithms. We are currently implementingmore advanced neural CPGs and will compare them in terms ofmotion robustness and traveling speed.This poster presents the neural inspiration behind the robot (2, 3) aswell as its physical design (4), discusses some experimental resultsin locomotion (5-7), and suggests some open questions for futureresearch (8).
Jörg Conradt, Rodney J. Douglas
Paulina Varshavskaya
Kerstin Preuschoff
, Institute of Neuroinformatics, ETH / University Zurich
, CSAIL, Massachusetts Institute of Technology
, CNS, California Institute of Technology
No paired fins sticking out
One wavelength / body length (of roughly 100 segments)Other examples for CPG-driven motion: Eels, Snakes
Oscillation frequency of body proportional to traveling speedDistributed CPG segments along the spinal cordLeft and right alternating activity, traveling uniformly downthe body
Intersegmental coupling to generate stable traveling wave
Pictures of lampreys Activity of CPGs in a 50 segmentisolated piece of spinal cord
A Mathematical Model for CPG-driven Locomotion
Data and Image courtesy of Avis Cohen, University of Maryland
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Frontal view of a segment
Sketch of the robot in side-view
1
Funded by the
and by travel grants from theInstitute of Neuroinformatics, ETH / University of Zurich
Neuromorphic Engineering’sResearch Collaborative Network (INE-RCN)
Experiments
Results
3 5
64
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The Lamprey, a Simple Example for CPG-driven Locomotion2
Discussion
Future Directions
Integrating local sensor signals
Implementing more detailed CPG algorithms
Adding an accelerometer, using signals to learnparameters for efficient forward motion
Stabilizing the head during motion (accelerometer)
Adding vision, processing data onboard (eg. simplecolor blob tracking), goal directed motion
Finding sparse coupling network
Commercializing the robot:
(adapting local CPG behavior to external stimuli)
targeting academia or the toy-market?
8
Cohen, A.H., Holmes, P.J., Rand, R.H. (1982). The Nature of
Dowling, K.J. (1997). Limbless Locomotion: Learning to Crawl
Conradt, J., Varshavskaya, P. (2003). Distributed Central Pattern
heCouplings B
Generator Control
tetween Segmental Oscillators of the Lamprey
Locomotion: A Mathematical Model, inVol.13, pp.345-369.
with a Snake Robot, PhD Thesis, CMU
for a Serpentine Robot. Joint InternationalConference on Artificial Neural Networks a Neural InformationProcessing (ICANN/ Istanbul, Turkey.
Spinal Generator forJournal of Mathematical Biology,
ndICONIP),
References:
7
�
�
�
�
Robot motion in different mechanical configurations:- planar horizontal robot (snake-like )- planar vertical robot (worm-like)- alternating horizontal and vertical rotating segments
(motion in 3D)
Parameters for generating motion:- frequency of oscillation in segments- amplitude of oscillation in segments- phase offset between neighboring segments- coupling coefficients between segments
Motion experiments in different environments:- on table with fixated head and tail-segments- on different surfaces, eg carpet, plastic,
wood, stone
Data collection:- hand-recorded overall velocity
(forward and sidewinding)- sequences of desired and
true angular positions
Large number of segments, high number ofdegrees-of-freedom, inherently scalable
Robust and failure tolerant system
Elegant control by an incredibly simple model
Highly complex motion patterns relative toalgorithmic cost
Different mechanical configurations possible
We do not suggest that the robot is a good approximationto a biological system. Rather, we hope that the distributednature of the robot’s control will allow for interestingexperiments pertaining to the function of the spinal cord orother, possibly artificial, neural system.
Further documentation and demonstration videos of therobot are available on the web at:http://www.ini.ethz.ch/~conradt/projects/WormBot
Current phase of the i CPG oscillatorth
Frequency of the i oscillatorth
Transform of instantaneous internal phase toexternal bending angle
Change of oscillator phase, depending on ,coupling coefficients along the spinal cord,phase offset between neighboring segments
32 oscillating CPGs, withand without synchronization.
Synchronization is enabled ( )at (red line), when the segments
start forming a traveling wave.t=0
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