Control & Sensing Research in the Intelligent Servosystems Lab

1. Control & Sensing Researchin the Intelligent Servosystems Lab P. S. Krishnaprasad Institute for Systems Research & Department of Electrical and Computer Engineering University of Maryland, College Park Summer 2008

2. 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)

3. What is Control?What is an Intelligent Servosystem?

4. Icons of their times Feedback is the principle that governs these systems

5. 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

6. 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; …

7. Control Inside Dean Kamen’s Segway Ralph Hollis’ Ballbot

8. Working with biologists to make aerial robots as capable as bats Dragon Eye UAV

9. 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.

10. Photo: courtesy of Michael Scanlon, ARL

11. 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

12. Sound following behavior

13. Front Back Demo Without front-back distinction With front-back distinction

14. 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

17. 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 System of simultaneous equations for pseudo-range can be reduced to a single quadratic equation.

19. Patterns in Cooperative Control (formations of UAVs)

20. Rectilinear and Circular Control Laws

21. 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.

22. Main Idea Set up interaction laws between moving frames in such a way as to realize desired patterns asymptotically.

23. Biological Analogy (Planar Law) Steering controls: 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.

24. Boundary-Following Simulation

25. Obstacle-Avoidance Simulation

26. Patterns in Conflict(tactical missiles)

27. 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

28. Batlab Flight Data (2) From GHKM (2005), preprint. (with permission) Target is preying mantis, Parasphendale agrionina. Hearing organ blocked by vaseline. T. K. Horiuchi

29. 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)

30. Simulations of Motion Camouflage Feedback Law

31. Diagnostics on Distance (from camouflage) Random evader case Sinusoidal case Initial fast transient for random evader case

32. 1. E.W. Justh and P.S. Krishnaprasad, “A simple control law for UAV formation flying,” Institute for Systems Research Technical Report TR 2002-38, 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. 3609-3614, 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. 5204-5209, 2004. 5. E.W. Justh and P.S. Krishnaprasad, “Natural frames and interacting particles in three dimensions”, to appear, Proc. 43rd IEEEConf. 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

33. 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. 607-616. 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.

34. Support • NRL, AFOSR Theme 1 programs, DOD - Army Research Office MURI2001 Program • NSF-NIH Collaborative Research in Computational Neuroscience (CRCNS2004)

35. Collaborators Eric Justh Fumin Zhang Timothy Horiuchi Cynthia Moss Mandyam Srinivasan Kaushik Ghose