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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.

How would you choose the best robotic vacuum cleaner?

Author  Erwin Prassler

Video ID : 729

This video identifies some criteria that a consumer might use to decide on the purchase of a specific domestic cleaning robot.

Chapter 0 — Preface

Bruno Siciliano, Oussama Khatib and Torsten Kröger

The preface of the Second Edition of the Springer Handbook of Robotics contains three videos about the creation of the book and using its multimedia app on mobile devices.

Bruno Siciliano — Interview, February 2017

Author  Bruno Siciliano

Video ID : 846

Bruno Siciliano, Editor of the Springer Handbook of Robotics, gives an interview during the One SpringerNature event in Barcelona on 7 February 2017.

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 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.

Global Conference on Educational Robotics and International Botball Tournament

Author  KIPR

Video ID : 241

GCER is a STEM-oriented robotics conference, in which the majority of the attendees, paper authors, and presenters are K-12 robotics students. Educator-paper tracks and technology-research tracks also occur. GCER is also the site of the International Botball Tournament, KIPR Open, aerial robots contests, and elementary-school robotics challenges. Some of the recent guest speakers at the conference have included Dr. Maja Mataric (human-robot interactions), Dr. Vijay Kumar (coordinated flying robots), and Dr. Hiroshi Ishiguro (androids). Details from: .

Chapter 4 — Mechanism and Actuation

Victor Scheinman, J. Michael McCarthy and Jae-Bok Song

This chapter focuses on the principles that guide the design and construction of robotic systems. The kinematics equations and Jacobian of the robot characterize its range of motion and mechanical advantage, and guide the selection of its size and joint arrangement. The tasks a robot is to perform and the associated precision of its movement determine detailed features such as mechanical structure, transmission, and actuator selection. Here we discuss in detail both the mathematical tools and practical considerations that guide the design of mechanisms and actuation for a robot system.

The following sections (Sect. 4.1) discuss characteristics of the mechanisms and actuation that affect the performance of a robot. Sections 4.2–4.6 discuss the basic features of a robot manipulator and their relationship to the mathematical model that is used to characterize its performance. Sections 4.7 and 4.8 focus on the details of the structure and actuation of the robot and how they combine to yield various types of robots. The final Sect. 4.9 relates these design features to various performance metrics.

SCARA robots

Author  Adept Technology Inc

Video ID : 644

Fig. 4.20 The Adept robot uses closed-loop control and variable-reluctance motors.

Chapter 15 — Robot Learning

Jan Peters, Daniel D. Lee, Jens Kober, Duy Nguyen-Tuong, J. Andrew Bagnell and Stefan Schaal

Machine learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors; conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in robot learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this chapter, we attempt to strengthen the links between the two research communities by providing a survey of work in robot learning for learning control and behavior generation in robots. We highlight both key challenges in robot learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our chapter lies on model learning for control and robot reinforcement learning. We demonstrate how machine learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

Learning motor primitives

Author  Jens Kober, Jan Peters

Video ID : 355

The video shows recent success in robot learning for two basic motor tasks, namely, ball-in-a-cup and ball paddling. The video illustrates Section 15.3.5 -- Policy Search, of the Springer Handbook of Robotics, 2nd edn (2016). Reference: J. Kober, J. Peters: Imitation and reinforcement learning - Practical algorithms for motor primitive learning in robotics, IEEE Robot. Autom. Mag. 17(2), 55-62 (2010)

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.

Asymmetric teleoperation of dual-arm mobile manipulator

Author  Pawel Malysz, Shahin Sirouspour

Video ID : 75

The video presents an experiment demonstrating a dual-master system to teleoperate a single-slave mobile manipulator system with haptic feedback for the remote-block transfer task.

Chapter 72 — Social Robotics

Cynthia Breazeal, Kerstin Dautenhahn and Takayuki Kanda

This chapter surveys some of the principal research trends in Social Robotics and its application to human–robot interaction (HRI). Social (or Sociable) robots are designed to interact with people in a natural, interpersonal manner – often to achieve positive outcomes in diverse applications such as education, health, quality of life, entertainment, communication, and tasks requiring collaborative teamwork. The long-term goal of creating social robots that are competent and capable partners for people is quite a challenging task. They will need to be able to communicate naturally with people using both verbal and nonverbal signals. They will need to engage us not only on a cognitive level, but on an emotional level as well in order to provide effective social and task-related support to people. They will need a wide range of socialcognitive skills and a theory of other minds to understand human behavior, and to be intuitively understood by people. A deep understanding of human intelligence and behavior across multiple dimensions (i. e., cognitive, affective, physical, social, etc.) is necessary in order to design robots that can successfully play a beneficial role in the daily lives of people. This requires a multidisciplinary approach where the design of social robot technologies and methodologies are informed by robotics, artificial intelligence, psychology, neuroscience, human factors, design, anthropology, and more.

Human-robot teaming in a search-and-retrieve task

Author  Cynthia Breazeal

Video ID : 555

This video shows an example from a human participant study examining the role of nonverbal social signals on human-robot teamwork for a complex search-and-retrieve task. In a controlled experiment, we examined the role of backchanneling and task complexity on team functioning and perceptions of the robots’ engagement and competence. Seventy three participants interacted with autonomous humanoid robots as part of a human-robot team: One participant, one confederate (a remote operator controlling an aerial robot), and three robots (2 mobile humanoids and an aerial robot). We found that, when robots used backchanneling, team functioning improved and the robots were seen as more engaged.

Chapter 61 — Robot Surveillance and Security

Wendell H. Chun and Nikolaos Papanikolopoulos

This chapter introduces the foundation for surveillance and security robots for multiple military and civilian applications. The key environmental domains are mobile robots for ground, aerial, surface water, and underwater applications. Surveillance literallymeans to watch fromabove,while surveillance robots are used to monitor the behavior, activities, and other changing information that are gathered for the general purpose of managing, directing, or protecting one’s assets or position. In a practical sense, the term surveillance is taken to mean the act of observation from a distance, and security robots are commonly used to protect and safeguard a location, some valuable assets, or personal against danger, damage, loss, and crime. Surveillance is a proactive operation,while security robots are a defensive operation. The construction of each type of robot is similar in nature with amobility component, sensor payload, communication system, and an operator control station.

After introducing the major robot components, this chapter focuses on the various applications. More specifically, Sect. 61.3 discusses the enabling technologies of mobile robot navigation, various payload sensors used for surveillance or security applications, target detection and tracking algorithms, and the operator’s robot control console for human–machine interface (HMI). Section 61.4 presents selected research activities relevant to surveillance and security, including automatic data processing of the payload sensors, automaticmonitoring of human activities, facial recognition, and collaborative automatic target recognition (ATR). Finally, Sect. 61.5 discusses future directions in robot surveillance and security, giving some conclusions and followed by references.

Tracking people for security

Author  Nikos Papanikolopoulos

Video ID : 683

Tracking of people in crowded scenes is challenging because people occlude each other as they walk around. The latest revision of the University of Minnesota's person tracker uses adaptive appearance models that explicitly account for the probability that a person may be partially occluded. All potentially occluding targets are tracked jointly, and the most likely visibility order is estimated (so we know the probability that person A is occluding person B). Target-size adaptation is performed using calibration information about the camera, and the reported target positions are made in real-world coordinates.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

A cobot in automobile assembly

Author  Prasad Akella, Nidamaluri Nagesh, Witaya Wannasuphoprasit, J. Edward Colgate, Michael Peshkin

Video ID : 821

Collaborative robots - cobots - are a new class of robotic devices for direct physical interaction with a human operator in a shared workspace. Cobots implement software-defined "virtual surfaces" which can guide human and payload motion. A joint project of General Motors and Northwestern University has brought an alpha prototype cobot into an industrial environment. This cobot guides the removal of an automobile door from a newly painted body prior to assembly. Because of tight tolerances and curved parts, the task requires a specific escape trajectory to prevent collision of the door with the body. The cobot's virtual surfaces provide physical guidance during the critical "escape" phase, while sharing control with the human operator during other task phases. (Video Proceedings of the Int. Conf. on Robotics and Automation, 1999)