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Chapter 63 — Medical Robotics and Computer-Integrated Surgery

Russell H. Taylor, Arianna Menciassi, Gabor Fichtinger, Paolo Fiorini and Paolo Dario

The growth of medical robotics since the mid- 1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. This chapter will discuss some major themes and illustrate them with examples from current and past research. Further reading providing a more comprehensive review of this rapidly expanding field is suggested in Sect. 63.4.

Medical robotsmay be classified in many ways: by manipulator design (e.g., kinematics, actuation); by level of autonomy (e.g., preprogrammed versus teleoperation versus constrained cooperative control), by targeted anatomy or technique (e.g., cardiac, intravascular, percutaneous, laparoscopic, microsurgical); or intended operating environment (e.g., in-scanner, conventional operating room). In this chapter, we have chosen to focus on the role of medical robots within the context of larger computer-integrated systems including presurgical planning, intraoperative execution, and postoperative assessment and follow-up.

First, we introduce basic concepts of computerintegrated surgery, discuss critical factors affecting the eventual deployment and acceptance of medical robots, and introduce the basic system paradigms of surgical computer-assisted planning, execution, monitoring, and assessment (surgical CAD/CAM) and surgical assistance. In subsequent sections, we provide an overview of the technology ofmedical robot systems and discuss examples of our basic system paradigms, with brief additional discussion topics of remote telesurgery and robotic surgical simulators. We conclude with some thoughts on future research directions and provide suggested further reading.

Variable stiffness manipulator based on layer jamming

Author  MIT/Samsung

Video ID : 832

A tubular, variable-stiffness structure designed for establishing a guide channel for single-port surgery. The thin-layered materials enables jamming stiffness more effectively in a very limited space.

Chapter 56 — Robotics in Agriculture and Forestry

Marcel Bergerman, John Billingsley, John Reid and Eldert van Henten

Robotics for agriculture and forestry (A&F) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.

Smart Seeder: An autonomous high-accuracy, seed planter for broad-acre crops

Author  Jay Katupitiya

Video ID : 131

This video shows highly accurate (within 2 cm) guidance of a tractor and an implement. The tractor is speed-controlled and follows a specified path very accurately. The implement is a seed planter which also follows the same path with the same accuracy. The implement has its own power unit. Its wheels are steerable and driven under force control as demanded by the force sensor at the hitch point. This relieves the tractor from having to pull the implement with full force, and hence it can be a smaller machine. Highly precise planting and path- following repeatability enables plant-level care which significantly reduce the chemical use, hence reducing adverse environmental effects and cost.

Chapter 6 — Model Identification

John Hollerbach, Wisama Khalil and Maxime Gautier

This chapter discusses how to determine the kinematic parameters and the inertial parameters of robot manipulators. Both instances of model identification are cast into a common framework of least-squares parameter estimation, and are shown to have common numerical issues relating to the identifiability of parameters, adequacy of the measurement sets, and numerical robustness. These discussions are generic to any parameter estimation problem, and can be applied in other contexts.

For kinematic calibration, the main aim is to identify the geometric Denavit–Hartenberg (DH) parameters, although joint-based parameters relating to the sensing and transmission elements can also be identified. Endpoint sensing or endpoint constraints can provide equivalent calibration equations. By casting all calibration methods as closed-loop calibration, the calibration index categorizes methods in terms of how many equations per pose are generated.

Inertial parameters may be estimated through the execution of a trajectory while sensing one or more components of force/torque at a joint. Load estimation of a handheld object is simplest because of full mobility and full wrist force-torque sensing. For link inertial parameter estimation, restricted mobility of links nearer the base as well as sensing only the joint torque means that not all inertial parameters can be identified. Those that can be identified are those that affect joint torque, although they may appear in complicated linear combinations.

Dynamic identification of Kuka KR270 : Trajectory without load

Author  Maxime Gautier

Video ID : 486

This video shows a trajectory without load used to identify the dynamic parameters of the links, load, joint drive gains and gravity compensator of a heavy industrial Kuka KR 270 manipulator. Details and results are given in the paper: A. Jubien, M. Gautier: Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013) pp. 1355-1360

Chapter 46 — Simultaneous Localization and Mapping

Cyrill Stachniss, John J. Leonard and Sebastian Thrun

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main perception problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot’s location. SLAM serves both of these purposes.

We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.

Fast iterative alignment of pose graphs

Author  Edwin Olson

Video ID : 444

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the MIT Killian Court data set. Reference: E. Olson, J. Leonard, S. Teller: Fast iterative alignment of pose graphs with poor initial estimates, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Orlando (2006), pp. 2262 - 2269; doi: 10.1109/ROBOT.2006.1642040.

Chapter 8 — Motion Control

Wan Kyun Chung, Li-Chen Fu and Torsten Kröger

This chapter will focus on the motion control of robotic rigid manipulators. In other words, this chapter does not treat themotion control ofmobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other chapters. In this chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional– integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robotmanipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

JediBot - Experiments in human-robot sword-fighting

Author  Torsten Kröger, Ken Oslund, Tim Jenkins, Dan Torczynski, Nicholas Hippenmeyer, Radu Bogdan Rusu, Oussama Khatib

Video ID : 759

Real-world sword-fighting between human opponents requires extreme agility, fast reaction time and dynamic perception capabilities. This video shows experimental results achieved with a 3-D vision system and a highly reactive control architecture which allowfs a robot to sword fight against human opponents. An online trajectory generator is used as an intermediate layer between low-level trajectory-following controllers and high-level visual perception. This architecture enables robots to react nearly instantaneously to the unpredictable human motions perceived by the vision system as well as to sudden sword contacts detected by force and torque sensors. Results show how smooth and highly dynamic motions are generated on-the-fly while using the vision and force/torque sensor signals in the feedback loops of the robot-motion controller. Reference: T. Kröger, K. Oslund, T. Jenkins, D. Torczynski, N. Hippenmeyer, R. B. Rusu, O. Khatib: JediBot - Experiments in human-robot sword-fighting, Proc. Int. Symp. Exp. Robot., Québec City (2012)

Chapter 27 — Micro-/Nanorobots

Bradley J. Nelson, Lixin Dong and Fumihito Arai

The field of microrobotics covers the robotic manipulation of objects with dimensions in the millimeter to micron range as well as the design and fabrication of autonomous robotic agents that fall within this size range. Nanorobotics is defined in the same way only for dimensions smaller than a micron. With the ability to position and orient objects with micron- and nanometer-scale dimensions, manipulation at each of these scales is a promising way to enable the assembly of micro- and nanosystems, including micro- and nanorobots.

This chapter overviews the state of the art of both micro- and nanorobotics, outlines scaling effects, actuation, and sensing and fabrication at these scales, and focuses on micro- and nanorobotic manipulation systems and their application in microassembly, biotechnology, and the construction and characterization of micro and nanoelectromechanical systems (MEMS/NEMS). Material science, biotechnology, and micro- and nanoelectronics will also benefit from advances in these areas of robotics.

Multibeam bilateral teleoperation of holographic optical tweezers

Author  Fumihito Arai

Video ID : 490

This video shows micro-bead being trapped and moved using a multibeam bilateral teleoperation system of holographic optical tweezers accelerated by a graphics processing unit. The micro-beads follow the trajectory of each haptic device, and the forces to which the micro-beads are subjected, which are generated by Stokes drag, are measured and fed back to an operator via the haptic devices. This real-time telexistence was quantitatively evaluated based on the time response of the trapped beads and the fedback forces. The demonstration of touching red blood cells shows the effectiveness of this system for biomedical application.

Chapter 46 — Simultaneous Localization and Mapping

Cyrill Stachniss, John J. Leonard and Sebastian Thrun

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main perception problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot’s location. SLAM serves both of these purposes.

We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.

DTAM: Dense tracking and mapping in real-time

Author  Richard Newcombe

Video ID : 452

This video shows DTAM: Dense tracking and mapping in real-time, a system for real-time, fully-dense visual tracking and reconstruction, described in Chap. 46.4, Springer Handbook of Robotics, 2nd edn (2016). Reference: R.A. Newcombe, S.J. Lovegrove, A.J. Davison: DTAM: Dense tracking and mapping in real-time. Int. Conf. Computer Vision (ICCV),, Barcelona (2011), pp. 2320–2327

Chapter 51 — Modeling and Control of Underwater Robots

Gianluca Antonelli, Thor I. Fossen and Dana R. Yoerger

This chapter deals with modeling and control of underwater robots. First, a brief introduction showing the constantly expanding role of marine robotics in oceanic engineering is given; this section also contains some historical backgrounds. Most of the following sections strongly overlap with the corresponding chapters presented in this handbook; hence, to avoid useless repetitions, only those aspects peculiar to the underwater environment are discussed, assuming that the reader is already familiar with concepts such as fault detection systems when discussing the corresponding underwater implementation. Themodeling section is presented by focusing on a coefficient-based approach capturing the most relevant underwater dynamic effects. Two sections dealing with the description of the sensor and the actuating systems are then given. Autonomous underwater vehicles require the implementation of mission control system as well as guidance and control algorithms. Underwater localization is also discussed. Underwater manipulation is then briefly approached. Fault detection and fault tolerance, together with the coordination control of multiple underwater vehicles, conclude the theoretical part of the chapter. Two final sections, reporting some successful applications and discussing future perspectives, conclude the chapter. The reader is referred to Chap. 25 for the design issues.

Dive with REMUS

Author  Woods Hole Oceanographic Institution

Video ID : 87

Travel with a REMUS 100 autonomous, underwater vehicle on a dive off the Carolina coast to study the connection between the physical processes in the ocean at the edge of the continental shelf and the things that live there. Video footage by Chris Linder. Funding by the Department of the Navy, Science & Technology; and Centers for Ocean Sciences Education Excellence (COSEE).