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Chapter 24 — Wheeled Robots

Woojin Chung and Karl Iagnemma

The purpose of this chapter is to introduce, analyze, and compare various wheeled mobile robots (WMRs) and to present several realizations and commonly encountered designs. The mobility of WMR is discussed on the basis of the kinematic constraints resulting from the pure rolling conditions at the contact points between the wheels and the ground. Practical robot structures are classified according to the number of wheels, and features are introduced focusing on commonly adopted designs. Omnimobile robot and articulated robots realizations are described. Wheel–terrain interaction models are presented in order to compute forces at the contact interface. Four possible wheel-terrain interaction cases are shown on the basis of relative stiffness of the wheel and terrain. A suspension system is required to move on uneven surfaces. Structures, dynamics, and important features of commonly used suspensions are explained.

An omnidirectional mobile robot with active caster wheels

Author  Woojin Chung

Video ID : 325

This video shows a holonomic omnidirectional mobile robot with two active and two passive caster wheels. Each active caster is composed of two actuators. The first actuator drives a wheel; the second actuator steers the wheel orientation. Although the mechanical structure of the driving mechanisms becomes a little complicated, conventional tires can be used for omnidirectional motions. Since the robot is overactuated, four actuators should be carefully controlled.

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.

Injury evaluation of human-robot impacts

Author  Sami Haddadin, Alin Albu-Schäffer, Michael Strohmayr, Mirko Frommberger, Gerd Hirzinger

Video ID : 608

In this video, several blunt impact tests are shown, leading to an assessment of which factors dominate injury severity. We will illustrate the effects that robot speed, robot mass, and constraints in the environment have on safety in human-robot impacts. It will be shown that the intuition about high-impact loads being transmitted by heavy robots is wrong. Furthermore, the conclusion is reached that free impacts are by far less dangerous than being crushed. Reference: S. Haddadin, A. Albu-Schäffer, M. Strohmayr, M. Frommberger, G. Hirzinger: Injury evaluation of human-robot impacts, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Pasadena (2008), pp. 2203 – 2204; doi: 10.1109/ROBOT.2008.4543534.

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.

Robot calibration using a touch probe

Author  Ilian Bonev

Video ID : 425

The video shows a kinematic calibration experiment using a touch probe. The system realizes the point-plan contact with different plans. The calibration is thus based on position contact without orientation.

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

Chapter 74 — Learning from Humans

Aude G. Billard, Sylvain Calinon and Rüdiger Dillmann

This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. We start with a brief historical overview of the field. We then summarize the various approaches taken to solve four main questions: when, what, who and when to imitate. We emphasize the importance of choosing well the interface and the channels used to convey the demonstrations, with an eye on interfaces providing force control and force feedback. We then review algorithmic approaches to model skills individually and as a compound and algorithms that combine learning from human guidance with reinforcement learning. We close with a look on the use of language to guide teaching and a list of open issues.

Demonstration by visual tracking of gestures

Author  Ales Ude

Video ID : 99

Demonstration by visual tracking of gestures. Reference: A. Ude: Trajectory generation from noisy positions of object features for teaching robot paths, Robot. Auton. Syst. 11(2), 113–127 (1993); URL: http://www.cns.atr.jp/~aude/movies/ .

Chapter 67 — Humanoids

Paul Fitzpatrick, Kensuke Harada, Charles C. Kemp, Yoshio Matsumoto, Kazuhito Yokoi and Eiichi Yoshida

Humanoid robots selectively immitate aspects of human form and behavior. Humanoids come in a variety of shapes and sizes, from complete human-size legged robots to isolated robotic heads with human-like sensing and expression. This chapter highlights significant humanoid platforms and achievements, and discusses some of the underlying goals behind this area of robotics. Humanoids tend to require the integration ofmany of the methods covered in detail within other chapters of this handbook, so this chapter focuses on distinctive aspects of humanoid robotics with liberal cross-referencing.

This chapter examines what motivates researchers to pursue humanoid robotics, and provides a taste of the evolution of this field over time. It summarizes work on legged humanoid locomotion, whole-body activities, and approaches to human–robot communication. It concludes with a brief discussion of factors that may influence the future of humanoid robots.

Dynamic multicontact motion

Author  Eiichi Yoshida

Video ID : 597

A method to plan optimal whole-body, dynamic motion in multicontact non-gaited transitions has been developed. Using a B-spline time parameterization for the active joints, we turn the motion-planning problem into a semi-infinite programming formulation which is solved by nonlinear optimization techniques. We address the problem of the balance within the optimization problem and demonstrate that generating whole-body multicontact dynamic motion for complex tasks is possible.

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.

IHMC/Yobotics biped

Author  Jerry Pratt

Video ID : 530

A 12 DOF biped walking robot driven by linear series elastic actuators developed by researchers at IHMC and Yobotics.

Chapter 54 — Industrial Robotics

Martin Hägele, Klas Nilsson, J. Norberto Pires and Rainer Bischoff

Much of the technology that makes robots reliable, human friendly, and adaptable for numerous applications has emerged from manufacturers of industrial robots. With an estimated installation base in 2014 of about 1:5million units, some 171 000 new installations in that year and an annual turnover of the robotics industry estimated to be US$ 32 billion, industrial robots are by far the largest commercial application of robotics technology today.

The foundations for robot motion planning and control were initially developed with industrial applications in mind. These applications deserve special attention in order to understand the origin of robotics science and to appreciate the many unsolved problems that still prevent the wider use of robots in today’s agile manufacturing environments. In this chapter, we present a brief history and descriptions of typical industrial robotics applications and at the same time we address current critical state-of-the-art technological developments. We show how robots with differentmechanisms fit different applications and how applications are further enabled by latest technologies, often adopted from technological fields outside manufacturing automation.

We will first present a brief historical introduction to industrial robotics with a selection of contemporary application examples which at the same time refer to a critical key technology. Then, the basic principles that are used in industrial robotics and a review of programming methods will be presented. We will also introduce the topic of system integration particularly from a data integration point of view. The chapter will be closed with an outlook based on a presentation of some unsolved problems that currently inhibit wider use of industrial robots.

SMErobotics Demonstrator D3 assembly with sensitive compliant robot arms

Author  Martin Haegele, Thilo Zimmermann, Björn Kahl

Video ID : 382

SMErobotics: Europe's leading robot manufacturers and research institutes have teamed up with the European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing - to make the vision of cognitive robotics a reality in a key segment of EU manufacturing. Funded by the European Union 7th Framework Programme under GA number 287787. Project runtime: 01.01.2012 - 30.06.2016 For a general introduction, please also watch the general SMErobotics project video (ID 260). About this video: Chapter 1: Introduction (0:00); Chapter 2: Work cell description and configuration (00:29); Chapter 3: Selection of the job (00:50); Chapter 4: Preparation step (01:09); Chapter 5: Riveting (01:44); Chapter 6: Error handling with automatic solution (02:17); Chapter 7: Finalise workflow (02:34); Chapter 8: Statement (03:09); Chapter 9: Outro (03:40); Chapter 10: The Consortium (03:54). For details, please visit: http://www.smerobotics.org/project/video-of-demonstrator-d3.html

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.

Coevolved predator and prey robots

Author  Dario Floreano

Video ID : 38

Coevolved predator and prey robots engaged in a tournament. The predator and prey robot (from left to right) are placed in an arena surrounded by walls and are allowed to interact for several trials starting at different, randomly-generated orientations. Predators are selected on the basis of the percentage of trials in which they are able to catch (i.e., to touch) the prey, and prey on the basis of the percentage of trials in which they were able to escape (i.e., to not be touched by) predators. Predators have a vision system, whereas the prey have only short-range distance sensors, but can go twice as fast as the predator. Collision between robots is detected by a conductive belt at the base of the robots.

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.

Social referencing behavior

Author  Cynthia Breazeal

Video ID : 556

This video is an example of how nonverbal and verbal communication, emotive behavior, and social learning integrate to support social referencing in human-robot interaction. The robot, Leonardo, learns the affective appraisal of two novel objects by reading the affective appraisal given by a person (via facial expression, tone of voice, and word choice). The robot uses joint attention mechanisms to understand the referent of the interaction, and learns to associate the affective appraisal with this novel object. The robot then uses its own emotive responses to engage with that object accordingly (e.g., approach and explore a positively appraised object, avoid a negatively appraised object).