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Chapter 43 — Telerobotics

Günter Niemeyer, Carsten Preusche, Stefano Stramigioli and Dongjun Lee

In this chapter we present an overview of the field of telerobotics with a focus on control aspects. To acknowledge some of the earliest contributions and motivations the field has provided to robotics in general, we begin with a brief historical perspective and discuss some of the challenging applications. Then, after introducing and classifying the various system architectures and control strategies, we emphasize bilateral control and force feedback. This particular area has seen intense research work in the pursuit of telepresence. We also examine some of the emerging efforts, extending telerobotic concepts to unconventional systems and applications. Finally,we suggest some further reading for a closer engagement with the field.

Single- and dual-arm supervisory and shared control

Author  Paul S. Schenker, Antal K. Bejczy, Won S. Kim

Video ID : 299

This video shows single- and dual-arm supervisory and shared teleoperation control for the remote repair of solar panels attached to a space satellite.

Chapter 55 — Space Robotics

Kazuya Yoshida, Brian Wilcox, Gerd Hirzinger and Roberto Lampariello

In the space community, any unmanned spacecraft can be called a robotic spacecraft. However, Space Robots are considered to be more capable devices that can facilitate manipulation, assembling, or servicing functions in orbit as assistants to astronauts, or to extend the areas and abilities of exploration on remote planets as surrogates for human explorers.

In this chapter, a concise digest of the historical overview and technical advances of two distinct types of space robotic systems, orbital robots and surface robots, is provided. In particular, Sect. 55.1 describes orbital robots, and Sect. 55.2 describes surface robots. In Sect. 55.3, the mathematical modeling of the dynamics and control using reference equations are discussed. Finally, advanced topics for future space exploration missions are addressed in Sect. 55.4.

DLR ROKVISS camera images pulling spring

Author  Gerd Hirzinger, Klaus Landzettel

Video ID : 334

ROKVISS manipulating the handles of springs to verify the long-term performance of the torque-controlled joints. The stereo-camera view from an actual robot end-effector is shown. At first, the robot performs an inspection maneuver. Then, it pulls on the spring in the experimental task board, with two different oscillation frequencies, to gain measurement data on the robot-joint's parameters (stiffness, temperature-dependent friction, etc.).

Chapter 32 — 3-D Vision for Navigation and Grasping

Danica Kragic and Kostas Daniilidis

In this chapter, we describe algorithms for three-dimensional (3-D) vision that help robots accomplish navigation and grasping. To model cameras, we start with the basics of perspective projection and distortion due to lenses. This projection from a 3-D world to a two-dimensional (2-D) image can be inverted only by using information from the world or multiple 2-D views. If we know the 3-D model of an object or the location of 3-D landmarks, we can solve the pose estimation problem from one view. When two views are available, we can compute the 3-D motion and triangulate to reconstruct the world up to a scale factor. When multiple views are given either as sparse viewpoints or a continuous incoming video, then the robot path can be computer and point tracks can yield a sparse 3-D representation of the world. In order to grasp objects, we can estimate 3-D pose of the end effector or 3-D coordinates of the graspable points on the object.

Google's Project Tango

Author  Google, Inc.

Video ID : 120

Google's Project Tango has been collaborating with robotics laboratories from around the world to synthesize the past decade of research and computer vision into the development of a new class of mobile devices. This video contains one of the first public announcements and presentations of a device that can be used for multiple robot-perception applications described in this chapter.

Chapter 76 — Evolutionary Robotics

Stefano Nolfi, Josh Bongard, Phil Husbands and Dario Floreano

Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.

A swarm-bot of eight robots displaying coordinated motion

Author  Stefano Nolfi, Gianluca Baldassarre, Vito Trianni, Francesco Mondada, Marco Dorigo

Video ID : 115

Each robot is provided with an independent neural controller which determines the desired speed of the two wheels on the basis of the traction force caused by the movements of the other robots. The evolved robots are able to display coordinated-motion capability, independent from the way in which they are assembled, as well as to coordinate in carrying heavy objects.

Chapter 40 — Mobility and Manipulation

Oliver Brock, Jaeheung Park and Marc Toussaint

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation,mobilemanipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning formobilemanipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

Motor-skill learning for robotics

Author  Jan Peters, Jens Kober, Katharina Mülling

Video ID : 667

We propose to divide the generic skill-learning problem into parts that can be well-understood from a robotics point of view. After appropriate learning approaches have been designed for these basic components, they will serve as the ingredients of a general approach to robot-skill learning. This video shows results of our work on learning to control, learning elementary movements, as well as steps towards the learning of complex tasks.

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 Staubli TX40 : Trajectory with load

Author  Maxime Gautier

Video ID : 481

This video shows a trajectory with a known payload mass of 4.5 kg attached to the end effector of an industrial Staubli TX 40 manipulator. Joint position and current reference data are collected on this short-time (8s) trajectory and used with data collected on a trajectory without load to identify all the dynamic parameters of the links, load and joint drive chain in a single global LS procedure. Details and results are given in the paper : M. Gautier, S. Briot: Global identification of joint drive gains and dynamic parameters of robots, ASME J. Dyn. Syst. Meas. Control 136(5), 051025̶ 051025-9 (2014); doi:10.1115/1.4027506

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.

A robot for harvesting sweet peppers in greenhouses

Author  Jochen Hemming, Wouter Bac, Bart van Tuijl, Ruud Barth, Eldert van Henten, Jan Bontsema, Erik Pekkeriet

Video ID : 304

This video shows robotic harvesting of sweet-pepper fruits in a commercial Dutch greenhouse in June 2014. The base of the robot consists of two carrier modules. On the first are located the manipulator (nine degrees-of-freedom), specifically developed for this project, the control electronics and the computers. On the sensor carrier module, two 5 megapixel color cameras (comprising a small baseline stereo setup) and a time-of-flight (TOF) camera are installed. Around the sensors, a light grid is placed to illuminate the scene. The sensor system is mounted on a linear motorized slide and can be horizontally moved in and out of the workspace of the manipulator. Machine-vision software localizes ripe fruits and obstacles in 3D. Two different types of end-effectors were designed and tested. The fin-ray gripper features a combined grip and cut mechanism. This end-effector first grips the fruit and after that the peduncle of the fruit is cut. The lip-type end-effector first stabilizes the fruit using a suction cup after which two rings enclose the fruit and cut the peduncle of the fruit. Both end-effectors have a miniature RGB and a TOF camera for refining the fruit position and to determine the fruit pose. This robot demonstrator is one of the results of the EU project CROPS, Clever Robots for Crops (www.crops-robots.eu).

Chapter 64 — Rehabilitation and Health Care Robotics

H.F. Machiel Van der Loos, David J. Reinkensmeyer and Eugenio Guglielmelli

The field of rehabilitation robotics considers robotic systems that 1) provide therapy for persons seeking to recover their physical, social, communication, or cognitive function, and/or that 2) assist persons who have a chronic disability to accomplish activities of daily living. This chapter will discuss these two main domains and provide descriptions of the major achievements of the field over its short history and chart out the challenges to come. Specifically, after providing background information on demographics (Sect. 64.1.2) and history (Sect. 64.1.3) of the field, Sect. 64.2 describes physical therapy and exercise training robots, and Sect. 64.3 describes robotic aids for people with disabilities. Section 64.4 then presents recent advances in smart prostheses and orthoses that are related to rehabilitation robotics. Finally, Sect. 64.5 provides an overview of recent work in diagnosis and monitoring for rehabilitation as well as other health-care issues. The reader is referred to Chap. 73 for cognitive rehabilitation robotics and to Chap. 65 for robotic smart home technologies, which are often considered assistive technologies for persons with disabilities. At the conclusion of the present chapter, the reader will be familiar with the history of rehabilitation robotics and its primary accomplishments, and will understand the challenges the field may face in the future as it seeks to improve health care and the well being of persons with disabilities.

Kineassist

Author  Discover Channel/Michael Peshkin

Video ID : 505

The Kineassist is a gait-training robot which rolls behind a patient and compliantly supports the trunk and pelvis. It enables patients to challenge the limits of their stability, catching them if they fall.

Chapter 21 — Actuators for Soft Robotics

Alin Albu-Schäffer and Antonio Bicchi

Although we do not know as yet how robots of the future will look like exactly, most of us are sure that they will not resemble the heavy, bulky, rigid machines dangerously moving around in old fashioned industrial automation. There is a growing consensus, in the research community as well as in expectations from the public, that robots of the next generation will be physically compliant and adaptable machines, closely interacting with humans and moving safely, smoothly and efficiently - in other terms, robots will be soft.

This chapter discusses the design, modeling and control of actuators for the new generation of soft robots, which can replace conventional actuators in applications where rigidity is not the first and foremost concern in performance. The chapter focuses on the technology, modeling, and control of lumped parameters of soft robotics, that is, systems of discrete, interconnected, and compliant elements. Distributed parameters, snakelike and continuum soft robotics, are presented in Chap. 20, while Chap. 23 discusses in detail the biomimetic motivations that are often behind soft robotics.

Active damping control on the DLR Hand Arm System

Author  Florian Petit, Alin Albu-Schäffer

Video ID : 548

The effectivness of active damping control is shown in a writing task performed by the DLR Hand Arm System.

Chapter 40 — Mobility and Manipulation

Oliver Brock, Jaeheung Park and Marc Toussaint

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation,mobilemanipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning formobilemanipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

Adaptive force/velocity control for opening unknown doors

Author  Yiannis Karayiannidis, Colin Smith, Francisco E. Vina, Petter Ogren, Danica Kragic

Video ID : 675

We propose a method that can open doors without prior knowledge of the door's kinematics. The method consists of a velocity controller that uses force measurements and estimates of the radial direction based on adaptive estimates of the position of the door hinge. The control action is decomposed into an estimated radial and tangential direction, following the concept of hybrid force/motion control.