Control & Sensing Research
in the
Intelligent Servosystems Lab
P. S. Krishnaprasad
Institute for Systems Research &
Department of Electrical and Computer Engineering
University of Maryland, College Park
Summer 2008
Outline
What is Control and what is an Intelligent
System?
Experiments and research program
Control and sensing test-beds
Fundamental problems
Multi-disciplinary science (biological
inspiration)
What is Control?
What is an Intelligent Servosystem?
Icons of their times
Feedback is the principle that governs these systems
Feedback
Feedback control is the principle that
governs self-organized systems
Feedback operates on sensory
information, creating a loop
Making feedback loops smart
is the task of engineers
Many Facets of Feedback
• Controlling a swarm of robots to do cooperative
things;
• Controlling the electric power grid reliably over
the internet;
• Controlling the myriad components (sensors,
electronics, motors) in a car via the onboard
computer;
• Controlling the distribution of medicine through
an implanted pump (thus saving lives);
• Controlling the environment by managing
effluents; …
Control Inside
Dean Kamen’s Segway
Ralph Hollis’ Ballbot
Working with biologists to make
aerial robots as capable as bats
Dragon Eye UAV
Binaural robots
DGPS vehicles
CHALLENGE:
Can we make a robot as
able a night-hunter as a
a barn owl?
Robots in our lab, that sense, using GPS, lasers, sonar etc.
Photo: courtesy of Michael Scanlon, ARL
Barn Owl and Robot
Can we capture the barn owl’s auditory acuity in a binaural robot?
2 degrees ~ microseconds resolution
In complex acoustic environments one needs ability to separate sources
Sound following behavior
Front Back Demo
Without front-back distinction
With front-back distinction
Acoustic Cues for Localization
•
Binaural/Inter-aural
Level/Intensity Difference (ILD)
Time/Phase Difference (IPD)
On-set difference/precedence effect
•
Monaural: spectral-directional filtering by
Pinna, mostly for elevation
•
Challenges from multi-aural perception
(soldier helmet)- separation of sources
Courtesy, Michael Scanlon, ARL
Cricket-in-the-loop feedback control
An indoor location sensing system for use by autonomous mobile
robots for navigation.
Positioning down to 5 cm, using a network of RF and ultrasound
beacons
Use of a Bancroft-type algorithm for fast localization.
Integration with MDLe (motion description language) and
odometry
P k  ( x k  x ) 2  ( y k  y ) 2  b,
k  1, 2,..., N
System of simultaneous equations for pseudo-range can be reduced
to a single quadratic equation.
Patterns in Cooperative Control
(formations of UAVs)
Rectilinear and Circular Control Laws
Maxwell and Gyroscopic Interaction
Maxwell’s equations are complemented by the equation of Lorentz
for the force on a charged particle in an electromagnetic field.
In a region where the electric field vanishes, the particle motion is
governed by a purely gyroscopic Lagrangian that includes a term
depending on the magnetic vector potential. Gyroscopic forces
leave the kinetic energy invariant.
Main Idea
Set up interaction laws between moving frames in such a
way as to realize desired patterns asymptotically.
Biological Analogy (Planar Law)
 r
 r

 r

 x1   y1   f (| r |)   y1    x2  y1
 | r |  | r | 
|r| 
Steering controls: u1   
 r
 r

 r

u2    x 2   y 2   f (| r |)  y 2   x1  y 2
|r |
 | r |

|r |

Align each vehicle
perpendicular to the baseline
between the vehicles.
Steer toward or away from
the other vehicle to maintain
appropriate separation.
Align with the
other vehicle’s
heading.
• Biological analogy (swarming, schooling):
- Decreasing responsiveness at large separation distances.
- Switch from attraction to repulsion based on
separation distance or density.
- Mechanism for alignment of headings.
D. Grünbaum, “Schooling as a strategy for taxis in a noisy environment,” in Animal Groups in
Three Dimensions, J.K. Parrish and W.M. Hamner, eds., Cambridge University Press, 1997.
Boundary-Following Simulation
Obstacle-Avoidance Simulation
Patterns in Conflict
(tactical missiles)
Batlab Flight Data (1)
Echolocating FM bat, Eptesicus fuscus
Courtesy of Cynthia Moss and Kaushik
Ghose, NACS, University of Maryland http://www.bsos.umd.edu/psyc/batlab/index.html
Batlab Flight Data (2)
From GHKM
(2005), preprint.
(with permission)
Target is preying mantis,
Parasphendale agrionina.
Hearing organ blocked by
vaseline.
T. K. Horiuchi
Dragonflies Aerial Battle Flight Data
a. Three dimensional reconstruction of
territorial interaction of two male
dragonflies Hemianax papuensis.
Shadower – blue; Shadowee – red
b, Angular-velocity profile produced by
the shadowing dragonfly in the shadowee's
eye (filled circles) compared with that
produced by a virtual stationary object
at the intersection point (hollow circles).
c. Another example – see frame 11 on
From A.K. Mizutani, J.S.Chahl,
and M.V. Srinivasan, “Motion
camouflage in dragonflies,”
Nature, vol. 423, p. 604, 2003.
(with permission)
Simulations of Motion Camouflage Feedback Law
Diagnostics on Distance (from camouflage)
Random evader case
Sinusoidal case
Initial fast transient for random evader case
References
1. E.W. Justh and P.S. Krishnaprasad, “A simple control law for UAV
formation flying,” Institute for Systems Research Technical Report TR 200238, 2002 (see http://www.isr.umd.edu).
2. E.W. Justh and P.S. Krishnaprasad, “Steering laws and continuum models
for planar formations,” Proc. 42nd IEEE Conf. Decision and Control, pp. 36093614, 2003.
3. E.W. Justh and P.S. Krishnaprasad, “Equilibria and steering laws for planar
formations,” Systems and Control Letters, Vol. 52, pp. 25-38, 2004.
4. F. Zhang, E.W. Justh, and P.S. Krishnaprasad, “Boundary following using
gyroscopic control,” Proc. 43rd IEEE Conf. Decision and Control, pp. 52045209, 2004.
5. E.W. Justh and P.S. Krishnaprasad, “Natural frames and interacting particles
in three dimensions”, to appear, Proc. 43rd IEEE Conf. Decision and Control,
also preprint arXiv:math.OC/0503390 v1, 8 pages, 2005.
6. F. Zhang, Geometric Cooperative Control of Formations, Ph.D. Thesis,
University of Maryland, 2004.
References
7. E.W. Justh and P.S. Krishnaprasad (2005). “Steering laws for motion
camouflage”, arXiv:math.OC/0508023
8. K. Ghose, T.K. Horiuchi, P.S. Krishnaprasad and C.F. Moss (2005).
“Echolcating bats capture insect prey using a nearly time-optimal
strategy”, preprint.
9. B. Afsari and P.S. Krishnaprasad (2004). “Some gradien based joint
diagonalization methods for ICA”, in G. Puntonet and A. Prieto
(eds.), ICA2004, Lecture Notes in Computer Science, vol. 3195,
pp.437-444.
10. A.A. Handzel and P.S. Krishnaprasad (2001). “Biomimetic sound
source localization”, IEEE Sensors Journal, vol. 2, no. 6, pp. 607616.
11. B. Azimi-Sadjadi and P.S. Krishnaprasad (2005). “Approximate
nonlinear filtering and its applications to navigation”, Automatica,
vol. 41, no. 6, pp. 945-956.
Support
• NRL, AFOSR Theme 1 programs, DOD Army Research Office MURI2001 Program
• NSF-NIH Collaborative Research in
Computational Neuroscience
(CRCNS2004)
Collaborators
Eric Justh
Fumin Zhang
Mandyam Srinivasan
Timothy Horiuchi Cynthia Moss
Kaushik Ghose