The Intelligent Servosystems Laboratory P. S. Krishnaprasad Department of Electrical and Computer Engineering Institute for Systems Research (ISR) University of Maryland, College Park July 25, 2003 INSTITUTE FOR SYSTEMS RESEARCH

BRIEF HISTORY 1986: Began as a small lab devoted to the study of –Control of flexible-link robotic arms (Westinghouse) –Tactile sensory processing for Si membrane sensor (NRL) –3-D graphics and related algorithms (NASA, AFOSR)

BRIEF HISTORY (cont’d) Evolution in A.V. Williams Bldg. as a lab devoted to the study of –actuators –manipulators –complex multi-body systems via physical experiments and simulations.

BRIEF HISTORY (cont’d) Highlights include experiments in –friction modeling and adaptive control (Westinghouse) –impact control (ARO) –design, fabrication and grasp analysis (NSF) –testbed for space robotics (NASA) –smart motor network (NSF) –walking robots (NASA, NSF)

Modular dextrous hand, (NSF, AFOSR) Loncaric, de- Comarmond,...

Hybrid motor (NSF, ARO), Venkataraman, Loncaric, Dayawansa, Krishnaprasad

Parallel manipulator (NASA, NSF, DOE) Tsai, Tahmasebi, Stamper,...

Snakeboard Video here

Roller Racer Movies Sean -straight line motion Sameer - circular arcs and figure eight

Paramecium

Joseph-Louis Lagrange Jean Le Rond d’Alembert Emmy Amalie Noether ( ) ( ) ( )

GOALS OF LAB To advance the state-of-the-art in design and real-time control of smart systems, drawing on advances in –Novel sensing and actuation materials and mechanism designs –New principles for actuation, propulsion, detection, reduction, learning and adaptation –Conceptualizing and prototyping across scales, to sense, actuate, communicate and control

RESEARCH & EDUCATION The lab as a facility for education and training –Education and training of some 30 M.S. students and 34 Ph.D. students since 1986, all of whom had participated in some significant way in the lab through experimental and computational investigations in addition to engaging in theoretical investigations.

RESEARCH & EDUCATION The lab as a facility for education and training –Involvement of undergraduates and high school students in the lab through REU programs and Young Scholar programs, continually, over the past 14 years.

ROLE IN ISR Focus on research in problems of interaction of physical systems with software systems –mobile robots with on-the-fly motion planning algorithms integrated approach to the design and control of smart systems –biologically inspired approaches to sensory processing and motion control

ROLE IN ISR Focus on research in the science of controllable materials –hysteresis from first principles –nonlinearity in adaptive optics A facility for research in real time control prototyping –dSPACE

Smart System Neuroscience Materials Intelligent Control Modeling & Optimization Wireless Noise & Sensors Smart Power MEMS Signal Processing Robotics LINKAGES

CURRENT PROJECTS Robotics (serial, parallel, mobile, small, …) –GPS-enhanced robotics Smart materials, devices and systems (CDCSS) –Hysteresis –Actuator arrays for adaptive optics (CDCSS and ARL)

CURRENT PROJECTS Smart manufacturing (NG, NSF) –Understand Si epitaxy, Si-Ge epitaxy via CFD –Process Control Links with biology (LIS, CAAR, NACS) –Learning and intelligence –Robotic barn-owl

ROBOTIC BARN OWL

GPS CAR

GPS CAR AND ANTENNA

MENTORING

GPS CONFIGURATION GPS is a satellite navigation system using NAVSTAR satellites Twenty-four operational satellites provide GPS receivers with satellite coverage at all times GPS Orbit Configuration

GPS-aided Location Determination and Navigation Receiver requires minimum of four satellites

GPS in Differential mode

Equations Distance from receiver to satellite given by: P i k =  i k + c [dt i - dt k ] + T i k + I i k + d i k + e i k P i k = pseudorange  i k = ||r i – r k || = distance between satellite and receiver = {(X i – X k ) 2 + (Y i – Y k ) 2 + (Z i – Z k ) 2 } 1/2 r i = position of receiver r k = position of satellite c = speed of light dt i = clock bias in receiver dt k = clock bias in satellite T i k = Tropospheric correction I i k = Ionospheric correction d i k = multipath correction e i k = noise Linearized form of Equation (Using Taylor series)  P i = -(U i k )  r i + C  dt i +  i k  P i = observed position – calculated position (U i k ) = unit vector from receiver to satellite  r i = actual position – initial estimate  dt i = actual receiver clock bias – initial estimate  i k = error terms Global to Local coordinates transformation -Sin  i *Cos i Sin i -Cos  i *Cos i n i = -Sin  i *Sin i e i = Cos i u I = -Cos  i *Sin i -Cos  i 0 -Sin  i