Robotics in Medical Applications 25 -11 25.6.3 Invasive Robotic Surgery ROBODOC ® Surgical Assistant 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 proceduresthattypically tendtobefullyinvasive 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 of1 mm or greater between the boneand the implant. ROBODOC aidsthe surgeon in shaping thepatient’s bone to match the implant to within 0.5mm. 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) Copyright © 2005 by CRC Press LLC 25 -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. HairTransplantationRobot: A roboticsystemusingimageguidance is being developedtoperform 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 Robotics 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. Copyright © 2005 by CRC Press LLC 26 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 Manufacturing 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. relativelysimplecontrolmethods.Eventcontrolwasoftenaccomplishedwithrelaylogic.Automaticcontrol 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, pro- 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 didnot appear untilthe 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 controlpotential. Along with this improvement came newer andfaster 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. Copyright © 2005 by CRC Press LLC Manufacturing Automation 26 -7 SHUTDOWNSECURITY LOCKOUT INITIALIZE MANUAL AUTO STOP Pyrometer Flame Detectors deg. C Laser Intens. SPINDLE TRAVERSE To p 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 Chemical Delivery Bubbler Systems 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 MFC01 MFC13 MFC14 MFC10 MFC11 MFC03 MFC06 MFC07 slpm slpm 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 AV03 AV08 AV12 AV14 AV16 AV07 AV11 AV13 AV04 AV02 FIGURE 26.5 HMI gas delivery sub-system menu example. Copyright © 2005 by CRC Press LLC Manufacturing 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. Copyright © 2005 by CRC Press LLC Index A 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 I-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 BrownBoveriLTD,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 Index 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 [...]... equations, 6-3 Kane’s equations, 6-4 in robotic literature, 6 -2 2 25 Kane’s method, 4 -2 , 6-1 29 commercial software packages related, 6 -2 5 29 description, 6-3 –4 discrete general steps, 6-5 kinematics, 6-1 8 22 preliminaries, 6-1 6–18 Kinematic(s), 1 7-3 –4, 2 4 -9 –11 chain, 1 7 -2 closed, 2 4-1 0 deformation, 2 4-1 0 design, 1 3-6 and dynamic models in closed form, 1 4-1 5–17 interfaces, 2 3-3 Kane’s method, 6-1 8 22 modeling,... (GAAT), 2 1-3 Index H Hair transplantation robot, 2 5-1 2 Hall effect sensor, 1 2- 8 , 1 2- 8 f Haptic interface to virtual environments, 2 3-1 21 , 2 3 -2 f applications, 2 3-3 –4 characterizing human user, 2 3-5 classification, 2 3 -2 –3 design, 2 3-7 9 related technologies, 2 3-1 2 specification and design of, 2 3-5 –7 system network diagram and block diagram, 2 3-5 f system performance metrics and specifications, 2 3-4 9 Haptic... derivation, 8-4 pathology, 2- 7 procedure, 3-4 representation, 2 1-1 4 transformation, 4-1 Density, 2 4-4 Desired object impedance, 2 0-8 f Detent torque, 1 2- 1 4 Determinism, 1 3-4 Device-level networks, 2 6-1 0–11 DeviceNet, 2 6-1 0 Devol, George C., 1-4 –5 Dexterity, 2 0 -2 f D-H See Denavit-Hartenberg (D-H) Dh.dat, 3-1 8, 3 -2 8 Different image surfaces, 2 2- 4 Digital sensors, 1 2- 1 0– 12 common uses for, 1 2- 1 1– 12 with NPN... assessment, 1 0-1 2 15 error sources, 1 0-5 –7, 1 0-6 t probability, 1 0 -2 –3 tolerances, 1 0-3 –5 Error dynamics block diagram, 1 7 -9 f Error equation, 1 7 -9 Error sources, 1 0-1 effects on roundness, 1 0-1 5f superposition of, 1 0-1 5f Essential matrix, 2 2- 4 –5, 2 2- 6 Ethernet, 2 6-1 1, 2 6-1 2, 2 6-1 2f Euclidean distance, 2- 1 Euler angles, 2- 4 , 1 7-4 Euler-Lagrange equations, 5-6 Euler’s equation of motion, 4-3 f I-5 Euler’s... 1 4 -2 map, 5-4 , 1 7-3 –4 Flexible arm kinematics of, 2 4 -2 0 Flexible exhaust hose, 2 1-3 Flexible robot arms, 2 4-1 – 42 design and operational strategies, 2 4-3 9 41 open and loop feedforward control command filtering, 2 4-3 2 35 Robotics and Automation Handbook I-6 Flexible robots trajectory planning, 9- 1 25 applications, 9- 1 3–14 Flight simulation, 2 3 -2 Fluid power actuators, 1 2- 1 7–18 Folded back, 3 -2 Food processing,... constraints, 5-1 1, 1 6-1 4–16 Homogeneous matrix, 5 -2 Homogeneous transformation, 2- 6 , 2- 7 computes C-code, 3 -2 4 25 , 3 -2 5 26 Homogeneous transformation, 4x4, 4-1 Homogeneoustransformation.c, 3-1 8, 3 -2 5 26 Homogeneous transformation matrices (HTM), 1 0-8 , 1 0 -9 , 1 0-1 0 algorithm for determining, 8-6 –8 Homogeneous vector, 5 -2 Homunculus, 1 -2 Honda, 1-1 1 I-7 Hooke’s law, 2 4 -2 Hose management arm (HMA), 2 1-3 HTM See... Geometric model, 2 3-1 7 Geometric vision survey, 2 2- 1 22 Global proximity test, 2 3-1 8 Global warming, 1-3 GM, 1 -2 , 1-5 , 1-7 Golem, 1-1 , 1 -2 Grafton, Craig, 2 1 -2 Graphical animation, 2 1-1 2 13 Graphical user interface (GUI), 2 6-6 Graphical visualization tools, 2 1-1 Grasping forces and friction, 1 1-1 2 13 Grasping modes endeffector, 1 1-1 1–13 Grasping stability, 1 1-1 1– 12 Grasp types for human hands, 1 1-1 2f Greek... sensors, 1 2- 5 Edinburgh Modular Arm System (EMAS), 1-1 1 Eigenfunctions, 2 4-1 8– 19 Eigenvalues and corresponding eigenfunctions, 2 4-1 8– 19 Eight-point linear algorithm, 2 2- 4 , 2 2- 5 coplanar features, 2 2- 7 –8 homography, 2 2- 7 –8 Eight-point structure from motion algorithm, 2 2- 6 Elastic averaging, 1 3-6 Elastic modulus, 2 4-3 –4 Elbow manipulator, 3-5 , 3-5 f link frame attachments, 3-5 f Electrical power, 1 1 -9 Electromagnetic... 3 -2 4 25 Frames of reference assigning, 2- 7 Frankenstein, 1 -2 Frankenstein, Victor, 1 -2 Free-body approach, 4-3 Freedom robot army manipulator, 8 -9 f Frequency domain solutions, 2 4-1 6– 19 Frequency response and impulse response, 2 4-1 9 FRFs magnitude plots of, 1 5-1 3f Friction in dynamics, 7 -2 1 22 and grasping forces, 1 1-1 2 13 Frictional forces, 1 9 -2 f Friction forces, 7-1 6–17 as result of contact, 7 -2 2f Friction... acceptability, 2 5-5 severity determination, 2 5-5 verification and validation, 2 5-4 Hazardous environments, 1 1-3 Headers C-code, 3 -2 9 Hebrew mythology, 1-1 HelpMate Robotics, 1-1 0 Hexaglide mechanism, 9 -2 f High end robot simulation packages, 2 1-7 –8 Highway addressable remote transducer (HART) sensor-level communications protocol, 2 6 -9 –10, 2 6-1 0f HMA, 2 1-3 HMI, 2 6-6 –8 Hohn, Richard, 1-8 Holding torque, 1 2- 1 4 Holonomic . reconstruct, 2 2-1 2f two images, 2 2-7 f two views, 2 2-7 f Camera calibration, 2 2-4 Camera model, 2 2 -2 –3 Camera poses cell structure recovered, 2 2 -2 1f CAN, 2 6-1 0 Capacitive displacement sensors, 1 2- 5 –6 distance. equation, 1 7 -9 Error sources, 1 0-1 effects on roundness, 1 0-1 5f superposition of, 1 0-1 5f Essential matrix, 2 2-4 –5, 2 2-6 Ethernet, 2 6-1 1, 2 6-1 2, 2 6-1 2f Euclidean distance, 2- 1 Euler angles, 2- 4 , 1 7-4 Euler-Lagrange. environments, 2 3-1 21 , 2 3 -2 f applications, 2 3-3 –4 characterizing human user, 2 3-5 classification, 2 3 -2 –3 design, 2 3-7 9 related technologies, 2 3-1 2 specification and design of, 2 3-5 –7 system network