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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 LWR : Trajectory without load

Author  Maxime Gautier

Video ID : 482

This video shows a trajectory without load used to identify the dynamic parameters of the links, load and torque sensor gain of the Kuka LWR manipulator. Details and results are given in the papers: A. Jubien, M. Gautier, A. Janot: Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data, preprint 19th IFAC World Congress, Cape Town (2014) pp. 8391-8396, M. Gautier, A. Jubien: Force calibration of the Kuka LWR-like robots including embedded joint torque sensors and robot structure, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Chicago (2014) pp. 416-421

Dynamic identification of a parallel robot : Trajectory without load

Author  Maxime Gautier

Video ID : 488

This video shows a trajectory without payload used to identify the dynamic parameters and joint drive gains of a parallel prototype robot Orthoglyde. Details and results are given in the paper : S. Briot, M. Gautier: Global identification of joint drive gains and dynamic parameters of parallel robots, Multibody Syst. Dyn. 33(1), 3-26 (2015); doi 10.1007/s11044-013-9403-6

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 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.

An example of a social robot in a museum

Author  Takayuki Kanda

Video ID : 808

This video shows a scene of interaction between a social robot in a science museum and museum visitors. The science museum would be one of the appropriate places for such a robot, because a novel robot would attract visitors' attention to the robot, which would also contribute to the purpose of the museum, i.e., to help visitors better understand science. Further, a social robot can redirect visitors' attention to museum exhibits by explaining museum exhibits.

Chapter 23 — Biomimetic Robots

Kyu-Jin Cho and Robert Wood

Biomimetic robot designs attempt to translate biological principles into engineered systems, replacing more classical engineering solutions in order to achieve a function observed in the natural system. This chapter will focus on mechanism design for bio-inspired robots that replicate key principles from nature with novel engineering solutions. The challenges of biomimetic design include developing a deep understanding of the relevant natural system and translating this understanding into engineering design rules. This often entails the development of novel fabrication and actuation to realize the biomimetic design.

This chapter consists of four sections. In Sect. 23.1, we will define what biomimetic design entails, and contrast biomimetic robots with bio-inspired robots. In Sect. 23.2, we will discuss the fundamental components for developing a biomimetic robot. In Sect. 23.3, we will review detailed biomimetic designs that have been developed for canonical robot locomotion behaviors including flapping-wing flight, jumping, crawling, wall climbing, and swimming. In Sect. 23.4, we will discuss the enabling technologies for these biomimetic designs including material and fabrication.

Robot dragonfly DelFly Explorer flies autonomously

Author  Christophe De Wagter, Sjoerd Tijmons, Bart D.W. Remes, Guido C.H.E. de Croon

Video ID : 402

The DelFly Explorer is the first flapping-wing micro air vehicle that is able to fly with complete autonomy in unknown environments. Weighing just 20 g, it is equipped with a 4 g onboard, stereo-vision system. The DelFly Explorer can perform an autonomous take-off, maintain its height, and avoid obstacles for as long as its battery lasts (~9 min). All sensing and processing is performed onboard, so no human or offboard computer is in the loop.

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.

LIBVISO: Visual odometry for intelligent vehicles

Author  Andreas Geiger

Video ID : 122

This video demonstrates a visual-odometry algorithm on the performance of the vehicle Annieway (VW Passat). Visual odometry is the estimation of a video camera's 3-D motion and orientation, which is purely based on stereo vision in this case. The blue trajectory is the motion estimated by visual odometry, and the red trajectory is the ground truth by a high-precision OXTS RT3000 GPS+IMU system. The software is available from http://www.cvlibs.net/

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.

Manus assistive robot

Author  Christopher Hamilton

Video ID : 500

The MIT-Manus assistive robot can be mounted on a wheelchair or a table to enable a user with paralysis to manipulate objects.

Chapter 17 — Limbed Systems

Shuuji Kajita and Christian Ott

A limbed system is a mobile robot with a body, legs and arms. First, its general design process is discussed in Sect. 17.1. Then we consider issues of conceptual design and observe designs of various existing robots in Sect. 17.2. As an example in detail, the design of a humanoid robot HRP-4C is shown in Sect. 17.3. To design a limbed system of good performance, it is important to take into account of actuation and control, like gravity compensation, limit cycle dynamics, template models, and backdrivable actuation. These are discussed in Sect. 17.4.

In Sect. 17.5, we overview divergence of limbed systems. We see odd legged walkers, leg–wheel hybrid robots, leg–arm hybrid robots, tethered walking robots, and wall-climbing robots. To compare limbed systems of different configurations,we can use performance indices such as the gait sensitivity norm, the Froude number, and the specific resistance, etc., which are introduced in Sect. 17.6.

Whegs II: A mobile robot using abstracted biological principles

Author  Roger D. Quinn

Video ID : 537

A leg-wheel robot developed by researchers at Case Western Reserve University.

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.

Machine learning table tennis

Author  Jan Peters, Katharina Mülling, Jens Kober, Oliver Kroemer, Zhikun Wang

Video ID : 354

The video shows recent successful demonstrations of using machine learning for robot table tennis. The first part shows learning of motor primitives for forehand strikes by training a robot with a mixture of imitation and reinforcement learning. The second part shows how the robot can anticipate an opponent's intended targets based on both forehand and backhand primitives. The video illustrates Sect. 15.3.5 Policy Search of the Springer Handbook of Robotics, 2nd edn (2016). Reference: K. Mülling, J. Kober, O. Kroemer, J. Peters: Learning to select and generalize striking movements in robot table tennis, Int. J. Robot. Res. 32(3), 263-279 (2013)

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.

The effect of twice dropping, and then gently placing, a two-gram weight on a small capacitive tactile array

Author  Mark Cutkosky

Video ID : 15

Video illustrating the effect of twice dropping, and then gently placing, a two-gram weight on a small capacitive tactile array sampled at 20 Hz. The first drop produces a large dynamic signal in comparison to the static load, but the second drop is missed, demonstrating the value of having dynamic tactile sensing.