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Chapter 18 — Parallel Mechanisms

Jean-Pierre Merlet, Clément Gosselin and Tian Huang

This chapter presents an introduction to the kinematics and dynamics of parallel mechanisms, also referred to as parallel robots. As opposed to classical serial manipulators, the kinematic architecture of parallel robots includes closed-loop kinematic chains. As a consequence, their analysis differs considerably from that of their serial counterparts. This chapter aims at presenting the fundamental formulations and techniques used in their analysis.

Par2 robot

Author  Sébastien Krut

Video ID : 51

This video demonstrates the Par2 robot, a high-speed planar parallel robot.

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: https://www.juniorbotballchallenge.org .

Chapter 13 — Behavior-Based Systems

François Michaud and Monica Nicolescu

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creature’s ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to explain behavior-based systems and their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 13.3. Section 13.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multirobot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

Experience-based learning of high-level task representations: Demonstration

Author  Monica Nicolescu

Video ID : 27

This is a video recorded in early 2000s, showing a Pioneer robot learning to visit a number of targets in a certain order - the human demonstration stage. The robot execution stage is also shown in a related video in this chapter. References: 1. M. Nicolescu, M.J. Mataric: Experience-based learning of task representations from human-robot interaction, Proc. IEEE Int. Symp. Comput. Intell. Robot. Autom. Banff (2001), pp. 463-468; 2. M. Nicolescu, M.J. Mataric: Learning and interacting in human-robot domains, IEEE Trans. Syst. Man Cybernet. A31(5), 419-430 (2001)

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.

Region-pointing gesture

Author  Takayuki Kanda

Video ID : 811

This short video explains what "region pointing" is. While it known that there are a variety of pointing gestures, in region pointing, unlike in other pointing gestures where the pointing arm is fixed, the arm moves as if it depicts a circle, which evokes the region it refers to.

Chapter 26 — Flying Robots

Stefan Leutenegger, Christoph Hürzeler, Amanda K. Stowers, Kostas Alexis, Markus W. Achtelik, David Lentink, Paul Y. Oh and Roland Siegwart

Unmanned aircraft systems (UASs) have drawn increasing attention recently, owing to advancements in related research, technology, and applications. While having been deployed successfully in military scenarios for decades, civil use cases have lately been tackled by the robotics research community.

This chapter overviews the core elements of this highly interdisciplinary field; the reader is guided through the design process of aerial robots for various applications starting with a qualitative characterization of different types of UAS. Design and modeling are closely related, forming a typically iterative process of drafting and analyzing the related properties. Therefore, we overview aerodynamics and dynamics, as well as their application to fixed-wing, rotary-wing, and flapping-wing UAS, including related analytical tools and practical guidelines. Respecting use-case-specific requirements and core autonomous robot demands, we finally provide guidelines to related system integration challenges.

AtlantikSolar field-trials

Author  Kostas Alexis

Video ID : 602

This video presents a small subset of the Autonomous Systems Lab (ASL) activities that took place during the ICARUS Field Trials in Marche-en-Famenne, Belgium, 8-12 September 2014. The ASL fixed-wing team went there with the solar-powered AtlantikSolar UAV and presented its advanced capabilities regarding long endurance flight, advanced estimation and control, autonomous navigation for complete coverage and its perception capabilities for search and rescue and map reconstruction. More information: http://www.fp7-icarus.eu/; www.asl.ethz.ch; www.atlantiksolar.ethz.ch

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 animation

Author  Gerd Hirzinger, Klaus Landzettel

Video ID : 333

ROKVISS: animated simulation of a two-joint, torque- controlled manipulator on the ISS as it performs an iInspection task. The split screen shows simulated views of the robot (at top) and the stereo camera's view of the robot end-effector (at bottom).

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.

VSA-Cube arm: Drawing on a wavy surface (low stiffness)

Author  centro di Ricerca "E. Piaggio"

Video ID : 473

A 3-DOF arm, built with VSA-cube units, performing a circle on a wavy surface with preset low stiffness.

Chapter 46 — Simultaneous Localization and Mapping

Cyrill Stachniss, John J. Leonard and Sebastian Thrun

This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main perception problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot’s location. SLAM serves both of these purposes.

We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.

Large-scale SLAM using the Atlas framework

Author  Michael Bosse

Video ID : 440

This video shows the operation of the Atlas framework for real-time, large-scale mapping using the MIT Killian Court data set. Atlas employed graphs of coordinate frames. Each vertex in the graph represents a local coordinate frame, and each edge represents the transformation between adjacent local coordinate frames. In each local coordinate frame, extended Kalman filter SLAM (Chap. 46.3.1, Springer Handbook of Robotics, 2nd edn 2016) is performed to make a map of the local environment and to estimate the current robot pose, along with the uncertainties of each. Each map's uncertainties were modelled with respect to its own local frame. Probabilities of entities in relation to arbitrary map-frames were generated by following a path formed by the edges between adjacent map-frames, using Dijkstra's shortest path algorithm. Loop-closing was achieved via an efficient map matching algorithm. Reference: M. Bosse, P. M. Newman, J. Leonard, S. Teller: Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework, Int. J. Robot. Res. 23(12), 1113-1139 (2004).

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.

SeaPerch Challenge 2014 'The Heist'

Author  Chris Hansen

Video ID : 634

This video shows one of the first runs of the 2014 SeaPerch Challenge, in which underwater, remotely-operated robots have to maneuver between obstacles while collecting objects from the floor of the pool.

Chapter 44 — Networked Robots

Dezhen Song, Ken Goldberg and Nak-Young Chong

As of 2013, almost all robots have access to computer networks that offer extensive computing, memory, and other resources that can dramatically improve performance. The underlying enabling framework is the focus of this chapter: networked robots. Networked robots trace their origin to telerobots or remotely controlled robots. Telerobots are widely used to explore undersea terrains and outer space, to defuse bombs and to clean up hazardous waste. Until 1994, telerobots were accessible only to trained and trusted experts through dedicated communication channels. This chapter will describe relevant network technology, the history of networked robots as it evolves from teleoperation to cloud robotics, properties of networked robots, how to build a networked robot, example systems. Later in the chapter, we focus on the recent progress on cloud robotics, and topics for future research.

A multi-operator, multi-robot teleoperation system

Author  Nak Young Chong

Video ID : 84

A multi-operator, multi-robot teleoperation system for collaborative maintenance operations: Video Proc. of ICRA 2001. Over the past decades, problems and notable results have been reported mainly in the single-operator single-robot (SOSR) teleoperation system. Recently, the need for cooperation has rapidly emerged in many possible applications such as plant maintenance, construction, and surgery, and considerable efforts have therefore been made toward the coordinated control of multi-operator, multi-robot (MOMR) teleoperation. We have developed coordinated control technologies for multi-telerobot cooperation in a common environment remotely controlled from multiple operators physically distant from each other. To overcome the operators' delayed visual perception arising from network throughput limitations, we have suggested several coordinated control aids at the local operator site. Operators control their master to get their telerobot to cooperate with the counterpart telerobot using the predictive simulator, as well as video image feedback. This video explains the details of the testbed and investigates the use of an online predictive simulator to assist the operator in coping with time delay.