1. 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

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 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 Feedback

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.  Inside Dean Kamen’s SegwayRalph Hollis’ Ballbot

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

9. DGPS vehicles Binaural robots Robots in our lab, that sense, using GPS, lasers, sonar etc. CHALLENGE: Can we make a robot as able a night-hunter as a a barn owl?

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 distinctionWith 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) 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. 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. 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. Steering controls:

24. Boundary-Following Simulation

25. Obstacle-Avoidance Simulation

26. Patterns in Conflict (tactical missiles)

27. Batlab Flight Data (1) Courtesy of Cynthia Moss and Kaushik Ghose, NACS, University of Maryland http://www.bsos.umd.edu/psyc/batlab/index.html Echolocating FM bat, Eptesicus fuscus

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

29. Dragonflies Aerial Battle Flight Data From A.K. Mizutani, J.S.Chahl, and M.V. Srinivasan, “Motion camouflage in dragonflies,” Nature, vol. 423, p. 604, 2003. (with permission) 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

30. Simulations of Motion Camouflage Feedback Law

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

32. References 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. 42 nd 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. 43 rd 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 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.

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