Robotics in Medical Applications 25 - 11As a final example in this chapter, we will look at the ROBODOC Surgical Assistant offered by Integrated Surgical Systems of Davis, California.. A
Trang 1Robotics in Medical Applications 25 - 11
As a final example in this chapter, we will look at the ROBODOC Surgical Assistant offered by Integrated Surgical Systems of Davis, California The ROBODOC system is used currently for procedures that typically tend to be fully invasive type of surgical procedures—total hip replacement and total knee replacement The system is designed to aid doctors with hip implants and other bone implants, through more accurate fitting and positioning The advantage currently offered by ROBODOC system is accuracy, which should translate into better patient outcomes According to Integrated Surgical Systems’ own literature, a typical surgical procedure without robotic assistance will routinely leave a gap of 1 mm or greater between the bone and the implant ROBODOC aids the surgeon in shaping the patient’s bone to match the implant to within 0.5 mm The ROBODOC system incorporates a computer planning system combined with a five-axis robot (see Figure 25.6) The robot carries a high-speed end-milling device to do the shaping One should note the theme of preplanning, which is pervasive in robotic surgery — given adequate information prior to the procedure (CT scans, MR scans, PET scans); a good planning component exploits the precision and degrees of freedom of a robot to offer a better technical option for the procedure.
Follow-up studies on ROBODOC cases support the fundamental thesis of robots in medicine of en-hanced outcomes: better fit and positioning of the implant to the bone (based on x-ray evaluations) with fewer fractures, as one might expect based on better fit and more accurate positioning With development
of newer technology, the ROBODOC system offers the potential for performing the surgery through a very small incision [Sahay et al 2004] of about 3 cm compared to standard incision sizes of about 15 cm Thus, even in the area of joint surgery that is typically an invasive procedure, robotic systems offer the potential for reducing invasiveness while maintaining the advantage of precision and accuracy.
FIGURE 25.6 ROBODOC Surgical Assistant System for hip replacement (Source: Integrated Surgical Systems)
Trang 225 - 12 Robotics and Automation Handbook
FIGURE 25.7 Artist’s rendering of robotic hair transplantation system (Source: Restoration Robotics, Inc.)
25.6.4 Upcoming Products
Robotics in medicine has been on the rise There will be newer products that employ robots in various different practices of medicine Two such new products that are in development are described here.
1 Hair Transplantation Robot: A robotic system using image guidance is being developed to perform
hair transplants Hair transplantation is a successful procedure that is performed routinely across the world The procedure involves transplanting 1000 to 2000 individual follicular units from a donor area of the patient (back of the head) to the target area of the patient (bald spot or thinning area on the head) The procedure is highly tedious, repetitive, and prone to errors due to fatigue
in the surgeon as well as the technicians A robotic system that automates this process is being developed by Restoration Robotics, Inc., Sunnyvale, California, which will eliminate the tedium, thus enhancing the quality of the transplants (Figure 25.7).
2 Robotic Catheter System: A telerobotic device is being developed to guide catheters in patients.
Cardiac surgery has undergone drastic changes in the past decade There are fewer and fewer open heart surgeries being performed and most of the problems related to the heart are being addressed
by delivering the appropriate treatment using catheters These procedures have become routine
in most of the hospitals However, guiding the catheter through the patient involves tedious work for the surgeon Furthermore, in order for the physician to observe the position of the catheter, the patient needs to be monitored using x-rays, which also exposes the surgeon while he or she
is guiding the catheter Hansen Medical, Palo Alto, California, is developing a robotic catheter system with broad capabilities as a standalone instrument or highly-controllable guide catheter to manipulate other minimally invasive instruments via a working lumen formed by the device The system has very sophisticated control and visualization aspects to enable an operator to navigate and conduct procedures remotely with high degrees of precision This system removes the tedium
in the procedure as well as enables the surgeon to stay out of the radiation field of the x-ray machine.
Bibliography
Adler, J.R., Frameless radiosurgery, in: Goetsch, S.J and DeSalles, A.A.F (eds.), Sterotactic Surgery and Radiosurgery, Medical Physics Publishing, Wisconsin, vol 17, pp 237–248, 1993.
Adler, J.R., Murphy, M.J., Chang, S.D., and Hancock, S.L., Image-guided robotic radiosurgery, Neuro-surgery, 44(6):1299–1307, June 1999.
Copyright © 2005 by CRC Press LLC
Trang 3Robotics in Medical Applications 25 - 13
Bodduluri, M and McCarthy, J.M X-ray guided robotic radiosurgery for solid tumors, Indus Robot J.,
29:3, March 2002.
Carts-Powell, Y., Robotics transforming the operating room, OE Reports (SPIE), 201, September 2000 Chenery, S.G., Chehabi, H.H., Davis, D.M., and Adler, J.R., The CyberKnife: beta system description and
initial clinical results, J Radiosurg., 1(4):241–249, 1998.
Larsson, B., Leksell, L., and Rexed, B., The high energy proton beam as a neurosurgical tool, Nature,
182:1222–1223, 1958.
Leksell, L., The stereotaxic method and radiosurgery of the brain, Acta Chir Scand., 102:316–319, 1951.
Murphy, M.J and Cox, R.S., The accuracy of dose localization for an image-guided frameless radiosugery
system, Med Phys., 23(12):2043–2049, 1996.
Murphy, M.J., Adler, J.R., Bodduluri, M., Dooley, J., Forster, K., Hai, J., Le, Q., Luxton, G., Martin, D., and
Poen, J., Image-guided radiosurgery for the spine and pancreas, Comput Aided Surg., 5:278–288,
2000.
Sahay, A., Witherspoon, L., and Bargar, W.L., Computer model-based study for minimally invasive THR
femoral cavity preparation using the ROBODOC system, Proceedings of the Computer-Aided Ortho-pedic Surgery Meeting, Chicago, IL, June 2004.
Schweikard, A., Adler, J.R., and Latombe, J.C., Motion planning in stereotaxic radiosurgery, Proceedings of the International Conference on Robotics and Automation, vol 9, pp 1909–1916, IEEE Press, 1993.
Schweikard, A., Tombropoulos, R.Z., Adler, J.R., and Latombe, J.C., Treatment planning for a
radiosur-gical system with general kinematics, Proceedings of the International Conference on Robotics and Automation, vol 10, pp 1720–1727, IEEE Press, 1994.
Sugano, N and Ochi, T., Medical robotics and computer-assisted surgery in the surgical treatment of patients with rheumatic diseases, www.rheuma21st.com, published April 27, 2000.
Tatter, S.B., History of stereotactic radiosurgery, http://neurosurgery.mgh.harvard.edu/hist-pb.htm, MGH Neurological Service, 1998.
World Robotics 2003, United Nations Economic Commission for Europe, October 2003.
Trang 426 Manufacturing Automation
Hodge Jenkins
Mercer University
26.1 Introduction 26.2 Process Questions for Control 26.3 Terminology
26.4 Hierarchy of Control and Automation
History
26.5 Controllers
PLC: Programmable Logic Controller • DCS: Distributed Control System • Hybrid Controller • Motion Controller
• PC-Based Open Controller
26.6 Control Elements
HMI: Human-Machine Interface • I/O: Inputs and Outputs
26.7 Networking and Interfacing
Sensor-Level I/O Protocol • Device-Level Networks
• Advanced Process Control Fieldbuses • Controller Networks • Information Networks and Ethernet • Selection
of Controllers and Networks
26.8 Programming
Ladder Logic Diagrams • Structured Text • Function Block Diagram • Sequential Flow Chart •IL: Instruction List
• Selection of Languages
26.9 Industrial Case Study 26.10 Conclusion
26.1 Introduction
As the global marketplace demands higher quality goods and lower costs, factory floor automation has been changing from separate machines with simple hardware-based controls, if any, to an integrated manufacturing enterprise with linked and sophisticated control and data systems For many organizations the transformation has been gradual, starting with the introduction of programmable logic controllers and personal computers to machines and processes However, for others the change has been rapid and
is still accelerating This chapter discusses the current state of control and data systems that make up manufacturing automation.
26.2 Process Questions for Control
The appropriate level of control and automation depends on the process to be automated Before this can
be accomplished, questions about the physical process and product requirements must be answered.
Copyright © 2005 by CRC Press LLC
Trang 5Manufacturing Automation 26 - 3
Advanced Loop Control
PID Loop Control
Event Control Motion Control
Enterprise Automation Quality Control & SPC
Process Control Multi-Process Control
FIGURE 26.1 Hierarchy of automation and control
relatively simple control methods Event control was often accomplished with relay logic Automatic control was all hardware-based, and as such it was not easily changed or improved.
As microprocessors became more prevalent and accepted in the later part of the 20th century, grammable logic controllers (PLC) were introduced and vastly improved process event control and pro-vided the ability to easily modify a process A separate and parallel action was programmable motion controllers With the increasing computational power of successive versions of microprocessors, propor-tional, integral, and derivative (PID) control was easily implemented in these controllers This allowed relatively easy tuning of servomechanisms Communication between the two controller types was initially analog signals, then serial data, and most recently one of several data networks While the first motion and process controllers were great milestones, integrated process and motion control with real-time process data availability did not appear until the late 1990s Critical processes, such as high speed drawing of optical fiber, required tightly couple motion and process control to manufacture competitively.
Thus, modern manufacturing automation systems joined motion control and process control together for greater flexibility and control potential Along with this improvement came newer and faster data buses,
Production Database (SQL Server)
Production server
Web-Based Production Report
Data Collection
Connection
Interactive Data Query, VB Applications
Financial Reporting
SPC Feedback PLC/HMI Control System
FIGURE 26.2 Manufacturing management information flow
Trang 6Manufacturing Automation 26 - 7
SHUTDOWN SECURITY LOCKOUT INITIALIZE MANUAL AUTO STOP
Pyrometer
Flame Detectors
deg C Laser Intens.
LIMITs
Home
mm/hr
mm/sec.
mm.
mm.
mm.
mm.
min sec Run Count
Recipe Status Info
Recipe Name:
Preform ID:
Speed:
Clad Torch Box Temp deg C
deg C Core Torch Box Temp
Position:
Speed:
Position:
End Burner
Outside Torch
Inside Torch
Current User:
Time in sequence
Time in Step
Phase Step #
Gas Mode #
Chm Mode #
min sec
Requested
Traverse Pass # Set length Current Length
Home Position
deg/sec deg.
Complete
Calculator
mm/hr
Averaging window
Avg Traverse Speed
Bottom
LASER ENABLE
Main Bulk Gas ChemicalDelivery SystemsBubbler
Sequence
&
Transitions Motion Trends PIDs Support
Systems
Current Traverse Speed
FIGURE 26.4 HMI main menu example
Bulk Gas System #1
CC06 GAS
AV17
TO BGS 2
SOLENOID VALVES
MV01
MV02
MFC14
MFC10
MFC11
MFC03
MFC06
MFC07
slpm
slpm
slpm
Inside
Inside
Outside
Outside
Endbumer
slpm
slpm
slpm Inside
Inside
Inside Outside
Outside
Inside
Endbumer
slpm
slpm
slpm
slpm
MFC02
MFC04
MFC05
MFC08
MV103
MV101
MV03
MV04
O2 Main
H2 Main
O2
AR
H2
AV01
AV05
AV06
AV09
AV10
AV15
AV12
AV14
AV16
AV07
AV11
AV13
AV04
AV02
FIGURE 26.5 HMI gas delivery sub-system menu example
Copyright © 2005 by CRC Press LLC
Trang 7Manufacturing Automation 26 - 19
References
[1] Bob Waterbury, DCS, PLC, PC, or PAS?, Control Eng., p 12, July 2001.
[2] Geller, D.A., Programmable Controllers using the Allen-Bradley SLC-500 Family, Prentice Hall, Upper
Saddle River, NJ, 2000.
[3] Piyevsky, S., Open network and automation products, Allen-Bradley Automation Fair, Anaheim, CA,
21 November 2002.
[4] Fielder, P.J and Schlib, C.J., Open architecture systems for robotic workcell integration, IWACT
1997 Conference Proceedings, Columbia, OH, 1997.
[5] Soft PLC Overview, URL: http://www.softplc.com/splcdata.htm.
[6] Mintchell, G.A., HMI/SCADA software-more than pretty pictures, Control Eng., 49, 18, December
2002.
[7] OPTO22 Factory Floor Software, v 3.1,D, OPTODisplay User Guide, Form 723-010216, OPTO22,
2001.
[8] Meldrum, N., ControlLogix®and HART protocol an integrated solution, Spectrum Controls, 2002 [9] Fieldbuses, look before you leap, EDN, p 197, 1998.
[10] URL: http://www.as-interface.com, 2003.
[11] Open DeviceNet Vendor Association (ODVA), URL: http://www.odva.org, 2003.
[12] Profibus International, URL: http://www.profibus.org, 2003.
[13] IEC 61158, Digital data communications for measurement and control — Fieldbus for use in in-dustrial control systems — Part 1: Overview and guidance, IEC, Geneva, 2003.
[14] ControlNet International, URL: http://www.controlnet.org, 2003.
[15] Foundation fieldbus, http://www.fieldbus.org, 2003.
[16] Lee, K.C and Lee, S., “Performance evaluation of switched Ethernet for real-time industrial
com-munications,” Computer Standards Interfaces, vol 24, no 5, pp 411–423, November 2002.
[17] IEC 61131-3, Programmable controllers — Part 3: Programming languages, IEC, Geneva, 2003 [18] IEC 61508-1, Functional safety of electrical/electronic/programmable electronic safety-related sys-tems — Part 1, IEC, Geneva, 1998.
[19] ANSI/ISA-S84.01-1996, Application of safety instrumented systems for the process industries, In-strument Society of America S84.01 Standard, Research Triangle Park, NC 27709, February 1996.
Trang 8A
A465, 11-4
AABB, 23-18
ABB, 1-8
Abb´e error (sine error), 13-5f
Abb´e principle, 13-4–5
Absolute coordinates
of vector x, 2-3
Absolute coordinate system, 20-3f
Absolute encoders, 12-3
example, 12-3f
Acceleration control for payload limits, 11-18
Accelerations, 4-9, 12-9–10
of center of mass, 4-6
online reconstruction of, 14-9–10
Acceptance procedures, 10-2
Accuracy, 13-3f
definition of, 13-2–3
AC&E’s CimStation Robotics, 21-7, 21-8
ACS, 24-36f, 24-37f
Active touch, 23-9, 23-11
Activity of force F, 6-4
Activity principle, 6-4
Actuator forces, 19-2f
Actuators, 12-12–18, 13-17
ADAMS
Kane’s method, 6-27
Adaptive command shaping (ACS), 24-36f, 24-37f
Adaptive feedback linearization, 17-16–18
Adjoint
Jacobian matrices, 2-12
Adjoint transformation, 5-3
Admittance regulation
vs impedance, 19-9–10
Advanced feedback control schemes, 24-29–31
with observers, 24-30–31
obstacles and objectives, 24-29–30
passive controller design with tip position feedback,
24-31
sliding mode control, 24-31
strain and strain rate feedback, 24-31
Advanced process control fieldbuses, 26-11
Affine connection, 5-10
Affine projection, 22-4
AI, 1-5 AIBO, 1-11 AIC, 1-5 Aliasing, 13-9–10 frequency-domain view of, 13-10f Alignment errors, 13-4–5
Al Qaeda, 1-10 Ambient temperature, 10-2 American Machine and Foundry, 1-7 AMF Corporation, 1-7
Analog displacement sensors, 12-4–5 Analog photoelectric, 12-7 Analog sensors, 12-4–10, 13-18–19 analog filtering, 13-19f Analog-to-digital conversion, 13-11 Analyzing coupled systems, 19-8–9 Angular error motions, 10-6t, 10-9f
Angular velocity and Jacobians associated with parametrized rotations,
2-8–10
ANSI Y14.5M, 10-3 Anticipatory control, 23-12–13 Approximations, 24-25 ARB IRB1400, 17-2f Aristotle, 23-10 ARMA, 14-13
Arm controller
robot end effector integrated into, 11-4f Arm degrees of freedom augmentation, 24-39–41 bracing strategies, 24-39
inertial damping, 24-40 piezoelectric actuation for damping, 24-41 Articulating fingers, 11-11
Artificial intelligence (AI), 1-5 Artificial Intelligence Center (AIC), 1-5 ASEA, Brown and Boveri (ABB), 1-8 ASEA Group, 1-8
Asimov, Isaac, 1-3–4, 1-4, 1-6 Asimov, Janet Jeppson, 1-4 Asimov, Stanley, 1-4
Assembly task
two parts by two arms, 20-10 Augmented dynamics-based control algorithm, 20-7, 20-7f
I-1
Trang 9I-2 Robotics and Automation Handbook
Augmented reality, 23-3
AUTOLEEV
Kane’s method, 6-27
Automated system
forming leads on electronic packages, 10-13f
leads location, 10-14f
Automatic calculator invention, 1-2
Automatic rifle, 1-2
Automatic symmetry cell
detection, matching and reconstruction, 22-18–21
Automaton, 1-3
Autoregressive moving-average (ARMA), 14-13
Axis, 5-3
Axis-aligned bounding boxes (AABB), 23-18
6-axis robot manipulator with five revolute joints, 8-13
B
Babbage, Charles, 1-2
Backward recursion, 4-2
Ball races, 12-13
Bar elements
distributed, 24-15
Bares, John, 1-7
Bargar, William, 1-10
Bars and compression, 24-5
Base frame, 2-3, 17-3
Base parameter set (BPS), 14-5
batch LS estimation, 14-7–8
element estimation, 14-7–8
estimation, 14-19–21
online gradient estimator, 14-8
Batch LS estimation
of BPS, 14-7–8
BBN criteria, 13-15
Beam elements in bending
distributed, 24-15–16
Beams and bending, 24-6–7
Bending deformation
geometry of, 24-6f
Bending transfer matrix, 24-16f
Bernoulli-Euler beam model, 6-21
Bernoulli-Euler beam theory, 6-16
Bezout identity, 17-14
Bilateral or force-reflecting teleoperator, 23-2
Body, 5-3–4
Body-fixed coordinate frame, 5-1
Body manipulator Jacobian matrix, 5-5
Bolt Beranek & Newman (BBN) criteria, 13-15
Bond graph modeling, 4-2
BPS See Base parameter set (BPS)
Bracing strategies
arm degrees of freedom augmentation, 24-39
Bridge crane example, 9-4–6
Broad phase, 23-18–19
Brooks, Rodney, 1-10
Brown Boveri LTD, 1-8
Buckling, 24-7–9
Building
reconstruction, 22-21f
C
Cable-driven Hexaglide, 9-1 Cable management, 13-7 CAD and graphical visualization tools, 21-1 Cadmus, 1-1
Calibration cube
four images used to reconstruct, 22-12f two images, 22-7f
two views, 22-7f Camera calibration, 22-4 Camera model, 22-2–3
Camera poses
cell structure recovered, 22-21f CAN, 26-10
Capacitive displacement sensors, 12-5–6 distance and area variation in, 12-6f Capek, Jose, 1-3
Capek, Karel, 1-3 Carl Sagan Memorial Station, 1-9 Carnegie Mellon University, 1-7 Cartesian error, 15-22f
Cartesian manipulator
stiffness control of, 16-5–6
Cell structure recovered
camera poses, 22-21f Centrifugal forces, 4-8 Centrifugal stiffening, 6-14
Characterizing human user
haptic interface to virtual environments, 23-5 Chasles’ Theorem, 2-5, 2-6, 5-3
Chatter free sliding control, 18-4–6 Chemical process control, 26-18f Christoffel symbols, 5-8, 5-10
of first kind, 17-5 CimStation Robotics, 21-2 CimStation simulated floor, 21-2f Cincinnati Milacron Corporation, 1-8 Closed-form equations, 4-7–8
Closed-form solutions
vs recursive IK solutions, 14-18f
Closed kinematic chains, 24-10 Collision detection, 23-17, 23-18–19 Collision detector, 23-17
flowchart, 23-18f Collision sensors, 11-17 Column buckling, 24-8 Combinations of loading, 24-7–9 Combined distributed effects and components, 24-16 Command generation, 9-4
Command shaping filter, 24-34
Common velocity
bond graph, 19-8f, 19-9f feedback representation, 19-8f, 19-9f Compensation based on system models, 23-15 Compliance based control algorithm, 20-6, 20-6f Compliant support of object, 20-8f
Composition of motions, 2-5 Compressed air, 11-8
Compression
and bars, 24-5
Trang 10Index I-3
Computational complexity reduction, 24-27
Computed torque, 17-8
Computed-torque control design, 15-5–6
Computejacobian.c, 3-18, 3-23–24
Conductive brushes, 12-15
Configuration, 5-2
infinite numbers
with none, 3-3f
with one, 3-3f
Configuration space, 17-3
Consolidated Controls Corporation, 1-5
Constrained Euler-Lagrange equation
geometric interpretation, 5-12
Constrained layer dampers, 13-15
Constrained systems, 5-11–13
Constraint(s), 13-6
Kane’s method, 6-14
Constraint connection, 5-12
Constraint distribution, 5-12
Constraint forces and torques
between interacting bodies, 7-15–16, 7-15f
Contents description, 24-2
Continuously elastic translating link, 6-17f
Continuous motion, 22-8
Continuous system
Kane’s method, 6-16
Control, 24-27
Control algorithms, 13-19–21
Control architecture, 17-7
Control bandwidth, 15-2
Control design, 16-5–6, 16-6–8, 16-12–14
with feedback linearization, 15-6–10
method taxonomy, 17-6–8
µ-synthesis feedback, 15-16–19
Control effort
tracking of various frequencies
with feedforward compensation, 9-20f
without feedforward compensation, 9-17
Controller(s)
experimental evaluation, 15-19–21
implementation, 13-16–17
networks, 26-11–12
selection of, 26-13
Controller area network (CAN), 26-10
ControlNet, 26-11, 26-12
Control system design, 17-8
Conventional controllers
bode plots of, 15-14f
Coordinated motion control
algorithm, 20-7–9
based on impedance control law, 20-7–10
of multiple manipulators
for handling an object, 20-5–7
problems of multiple manipulators, 20-5–7
Coordinate frames, 8-3, 8-13
schematic, 8-3
Coordinate measuring machine
deflection of, 9-3f
Coordinate systems, 20-3f
associated with link n, 4-3f
Coriolis centrifugal forces, 5-8
Coriolis effect, 4-7 Coriolis force, 4-8 Coriolis matrix, 5-8 Corless-Leitmann approach, 17-14 Correlation among multiple criteria, 10-13–14
Cosine error
example of, 13-4f CosmosMotion, 21-10 cost, 21-10 Coupled stability, 19-10–13 Coupled system stability analysis, 19-10
Couples systems poles
locus of, 19-13f Covariant derivative, 5-10
CPS
of tracking errors, 15-20 Craig notation and nomenclature, 3-3 Crane response to pressing move button, 9-5f Crane response to pressing move button twice, 9-5f Critical curve, 10-16
calculating points on, 10-18f Critical surface, 22-8 Cross-over frequencies, 15-18t Ctesibus of Alexandria, 1-2
Cube
reconstruction from single view, 22-17f
Cube drawing
example, 21-12
Cumulative power spectra (CPS)
of tracking errors, 15-20 Cutting tool, 10-16f envelope surface, 10-16f
as surface of revolution, 10-17f swept volume, 10-16f CyberKnife stereotactic radiosurgery system, 25-6–9, 25-7f accuracy and calibration, 25-9
computer software, 25-8–9 patient positioning, 25-8 patient safety, 25-9 radiation source, 25-7 robotic advantage, 25-9 robot manipulator, 25-7 stereo x-ray imaging system, 25-8 treatment planning system for, 25-8, 25-8f
D
DADS, 21-10 Damping, 24-4–5
inertial
arm degrees of freedom augmentation, 24-40 three axis arm as micromanipulator for, 24-41f
inertial controller
quenching flexible base oscillations, 24-41f passive, 24-39, 24-40f
sectioned constraining layer, 24-39f
piezoelectric actuation for
arm degrees of freedom augmentation, 24-41 Dante, 1-7
Dante II, 1-7 DARPA, 1-6