View Chapter

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 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.

Passive teleoperation of a nonlinear telerobot with tool-dynamics rendering

Author  Dongjun Lee

Video ID : 74

This is a video showing the passive teleoperation of nonlinear master-slave robots using passive decomposition, which enables master-slave coordination, apparent inertia scaling, and tool-dynamics rendering.

Chapter 19 — Robot Hands

Claudio Melchiorri and Makoto Kaneko

Multifingered robot hands have a potential capability for achieving dexterous manipulation of objects by using rolling and sliding motions. This chapter addresses design, actuation, sensing and control of multifingered robot hands. From the design viewpoint, they have a strong constraint in actuator implementation due to the space limitation in each joint. After briefly introducing the overview of anthropomorphic end-effector and its dexterity in Sect. 19.1, various approaches for actuation are provided with their advantages and disadvantages in Sect. 19.2. The key classification is (1) remote actuation or build-in actuation and (2) the relationship between the number of joints and the number of actuator. In Sect. 19.3, actuators and sensors used for multifingered hands are described. In Sect. 19.4, modeling and control are introduced by considering both dynamic effects and friction. Applications and trends are given in Sect. 19.5. Finally, this chapter is closed with conclusions and further reading.

DLR hand

Author  DLR -Robotics and Mechatronics Center

Video ID : 768

A DLR hand

Chapter 45 — World Modeling

Wolfram Burgard, Martial Hebert and Maren Bennewitz

In this chapter we describe popular ways to represent the environment of a mobile robot. For indoor environments, which are often stored using two-dimensional representations, we discuss occupancy grids, line maps, topologicalmaps, and landmark-based representations. Each of these techniques has its own advantages and disadvantages. Whilst occupancy grid maps allow for quick access and can efficiently be updated, line maps are more compact. Also landmark-basedmaps can efficiently be updated and maintained, however, they do not readily support navigation tasks such as path planning like topological representations do.

Additionally, we discuss approaches suited for outdoor terrain modeling. In outdoor environments, the flat-surface assumption underling many mapping techniques for indoor environments is no longer valid. A very popular approach in this context are elevation and variants maps, which store the surface of the terrain over a regularly spaced grid. Alternatives to such maps are point clouds, meshes, or three-dimensional grids, which provide a greater flexibility but have higher storage demands.

Learning navigation cost grids

Author  John Rebula

Video ID : 271

The video shows how the LittleDog robot of IHMC learns a terrain cost map based on several surface parameters. The map is then used for determining foot placements for the robot to enable it to traverse rough terrain.

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.

BLUE: A biped robot with variable stiffness and damping

Author  Chengxu Zhou, Xin Wang, Zhibin Li, Nikolaos Tsagarakis

Video ID : 466

The video includes clips of the robot walking, squatting while displaying various degrees of stiffness, and the stiffness adjustment mechanism.

Chapter 65 — Domestic Robotics

Erwin Prassler, Mario E. Munich, Paolo Pirjanian and Kazuhiro Kosuge

When the first edition of this book was published domestic robots were spoken of as a dream that was slowly becoming reality. At that time, in 2008, we looked back on more than twenty years of research and development in domestic robotics, especially in cleaning robotics. Although everybody expected cleaning to be the killer app for domestic robotics in the first half of these twenty years nothing big really happened. About ten years before the first edition of this book appeared, all of a sudden things started moving. Several small, but also some larger enterprises announced that they would soon launch domestic cleaning robots. The robotics community was anxiously awaiting these first cleaning robots and so were consumers. The big burst, however, was yet to come. The price tag of those cleaning robots was far beyond what people were willing to pay for a vacuum cleaner. It took another four years until, in 2002, a small and inexpensive device, which was not even called a cleaning robot, brought the first breakthrough: Roomba. Sales of the Roomba quickly passed the first million robots and increased rapidly. While for the first years after Roomba’s release, the big players remained on the sidelines, possibly to revise their own designs and, in particular their business models and price tags, some other small players followed quickly and came out with their own products. We reported about theses devices and their creators in the first edition. Since then the momentum in the field of domestics robotics has steadily increased. Nowadays most big appliance manufacturers have domestic cleaning robots in their portfolio. We are not only seeing more and more domestic cleaning robots and lawn mowers on the market, but we are also seeing new types of domestic robots, window cleaners, plant watering robots, tele-presence robots, domestic surveillance robots, and robotic sports devices. Some of these new types of domestic robots are still prototypes or concept studies. Others have already crossed the threshold to becoming commercial products.

For the second edition of this chapter, we have decided to not only enumerate the devices that have emerged and survived in the past five years, but also to take a look back at how it all began, contrasting this retrospection with the burst of progress in the past five years in domestic cleaning robotics. We will not describe and discuss in detail every single cleaning robot that has seen the light of the day, but select those that are representative for the evolution of the technology as well as the market. We will also reserve some space for new types of mobile domestic robots, which will be the success stories or failures for the next edition of this chapter. Further we will look into nonmobile domestic robots, also called smart appliances, and examine their fate. Last but not least, we will look at the recent developments in the area of intelligent homes that surround and, at times, also control the mobile domestic robots and smart appliances described in the preceding sections.

PhillieBot Robot throws out the first pitch at a Phillies game

Author  Erwin Prassler

Video ID : 748

PhillieBot, developed by University of Pennsylvania, throws out the first pitch at a Phillies' baseball game (alas, in the dirt).

Chapter 28 — Force and Tactile Sensing

Mark R. Cutkosky and William Provancher

This chapter provides an overview of force and tactile sensing, with the primary emphasis placed on tactile sensing. We begin by presenting some basic considerations in choosing a tactile sensor and then review a wide variety of sensor types, including proximity, kinematic, force, dynamic, contact, skin deflection, thermal, and pressure sensors. We also review various transduction methods, appropriate for each general sensor type. We consider the information that these various types of sensors provide in terms of whether they are most useful for manipulation, surface exploration or being responsive to contacts from external agents.

Concerning the interpretation of tactile information, we describe the general problems and present two short illustrative examples. The first involves intrinsic tactile sensing, i. e., estimating contact locations and forces from force sensors. The second involves contact pressure sensing, i. e., estimating surface normal and shear stress distributions from an array of sensors in an elastic skin. We conclude with a brief discussion of the challenges that remain to be solved in packaging and manufacturing damage-tolerant tactile sensors.

Capacitive tactile sensing

Author  Mark Cutkosky

Video ID : 14

Video demonstrating the capacitive tactile sensing suite on the SRI-Meka-Stanford four-fingered hand built for the DARPA ARM-H Mobile Manipulation program.

Chapter 30 — Sonar Sensing

Lindsay Kleeman and Roman Kuc

Sonar or ultrasonic sensing uses the propagation of acoustic energy at higher frequencies than normal hearing to extract information from the environment. This chapter presents the fundamentals and physics of sonar sensing for object localization, landmark measurement and classification in robotics applications. The source of sonar artifacts is explained and how they can be dealt with. Different ultrasonic transducer technologies are outlined with their main characteristics highlighted.

Sonar systems are described that range in sophistication from low-cost threshold-based ranging modules to multitransducer multipulse configurations with associated signal processing requirements capable of accurate range and bearing measurement, interference rejection, motion compensation, and target classification. Continuous-transmission frequency-modulated (CTFM) systems are introduced and their ability to improve target sensitivity in the presence of noise is discussed. Various sonar ring designs that provide rapid surrounding environmental coverage are described in conjunction with mapping results. Finally the chapter ends with a discussion of biomimetic sonar, which draws inspiration from animals such as bats and dolphins.

Vergence sonar

Author  Roman Kuc

Video ID : 301

Two conventional Polaroid sonars are oriented away from the sonar axis by a vergence angle of eight degrees and excited simultaneously every 100 ms. Simple logic determines which sonar detects the echo first - indicated by the red LEDs - and when the echoes arrive within a 3 µs window - indicated by the center yellow LED. The video indicates echoes from the ceiling located at 2 m range. The vergence sonar can determine normal incidence within 0.5 degree over a usable beam width of 46 degrees. Reference: R. Kuc: Binaural sonar electronic travel aid provides vibrotactile cues for landmark, reflector motion and surface texture classification, IEEE Trans. Biomed. Eng. 49(10), 1173-1180 (2002).

Chapter 79 — Robotics for Education

David P. Miller and Illah Nourbakhsh

Educational robotics programs have become popular in most developed countries and are becoming more and more prevalent in the developing world as well. Robotics is used to teach problem solving, programming, design, physics, math and even music and art to students at all levels of their education. This chapter provides an overview of some of the major robotics programs along with the robot platforms and the programming environments commonly used. Like robot systems used in research, there is a constant development and upgrade of hardware and software – so this chapter provides a snapshot of the technologies being used at this time. The chapter concludes with a review of the assessment strategies that can be used to determine if a particular robotics program is benefitting students in the intended ways.

New Mexico Elementary Botball 2014 - Teagan's first-ever run.

Author  Jtlboys3

Video ID : 635

This video shows some elementary-school students running their line-following code (written in C) on a robot at the local Junior Botball Challenge event. Details from: .

Chapter 53 — Multiple Mobile Robot Systems

Lynne E. Parker, Daniela Rus and Gaurav S. Sukhatme

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Reconfigurable multi-agents with distributed sensing for robust mobile robots

Author  Robin Murphy

Video ID : 206

In marsupial teams, a mother robot carries one or more daughter robots. This video shows that a mother robot can opportunistically treat daughter robots as surrogate sensors in order to autonomously reconfigure herself to recover from sensor failures.