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Chapter 20 — Snake-Like and Continuum Robots

Ian D. Walker, Howie Choset and Gregory S. Chirikjian

This chapter provides an overview of the state of the art of snake-like (backbones comprised of many small links) and continuum (continuous backbone) robots. The history of each of these classes of robot is reviewed, focusing on key hardware developments. A review of the existing theory and algorithms for kinematics for both types of robot is presented, followed by a summary ofmodeling of locomotion for snake-like and continuum mechanisms.

Automatic insertion implant calibration

Author  Nabil Simaan

Video ID : 245

Video shows a steerable model of electrode arrays for cochlear implant surgery. The implant is built from an elastomeric body with an embedded Kevlar strand. The strand location controls the bending shape in 2-D and 3-D. The video shows one model that moves in plane [1, 2]. In [1] we reported the optimal planning of the insertion path. In [2] we reported the optimal strand location to achieve optimal insertion in 3-D cavities. References: [1] J. Zhang, J. T. Roland, S. Manolidis, N. Simaan: Optimal path planning for robotic insertion of steerable electrode arrays in cochlear implant surgery, J. Med. Dev. 3(1), 011001 (2009); [2] J. Zhang, N. Simaan: Design of underactuated steerable electrode arrays for optimal insertions, J. Mech. Robot. 5(1), 011008 (2013)

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.

Demonstrations and reproduction of the task of juicing an orange

Author  Florent D'Halluin, Aude Billard

Video ID : 29

Human demonstrations of the task of juicing an orange, and reproductions by the robot in new situations where the objects are located in positions not seen in the demonstrations. URL: http://www.scholarpedia.org/article/Robot_learning_by_demonstration

Chapter 34 — Visual Servoing

François Chaumette, Seth Hutchinson and Peter Corke

This chapter introduces visual servo control, using computer vision data in the servo loop to control the motion of a robot. We first describe the basic techniques that are by now well established in the field. We give a general overview of the formulation of the visual servo control problem, and describe the two archetypal visual servo control schemes: image-based and pose-based visual servo control. We then discuss performance and stability issues that pertain to these two schemes, motivating advanced techniques. Of the many advanced techniques that have been developed, we discuss 2.5-D, hybrid, partitioned, and switched approaches. Having covered a variety of control schemes, we deal with target tracking and controlling motion directly in the joint space and extensions to under-actuated ground and aerial robots. We conclude by describing applications of visual servoing in robotics.

2.5-D VS on a 6-DOF robot arm (1)

Author  Francois Chaumette, Seth Hutchinson, Peter Corke

Video ID : 64

This video shows a 2.5-D VS on a 6-DOF robot arm with (x_g, log(Z_g), theta u) as visual features. It corresponds to the results depicted in Figure 34.12.

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 14 — AI Reasoning Methods for Robotics

Michael Beetz, Raja Chatila, Joachim Hertzberg and Federico Pecora

Artificial intelligence (AI) reasoning technology involving, e.g., inference, planning, and learning, has a track record with a healthy number of successful applications. So can it be used as a toolbox of methods for autonomous mobile robots? Not necessarily, as reasoning on a mobile robot about its dynamic, partially known environment may differ substantially from that in knowledge-based pure software systems, where most of the named successes have been registered. Moreover, recent knowledge about the robot’s environment cannot be given a priori, but needs to be updated from sensor data, involving challenging problems of symbol grounding and knowledge base change. This chapter sketches the main roboticsrelevant topics of symbol-based AI reasoning. Basic methods of knowledge representation and inference are described in general, covering both logicand probability-based approaches. The chapter first gives a motivation by example, to what extent symbolic reasoning has the potential of helping robots perform in the first place. Then (Sect. 14.2), we sketch the landscape of representation languages available for the endeavor. After that (Sect. 14.3), we present approaches and results for several types of practical, robotics-related reasoning tasks, with an emphasis on temporal and spatial reasoning. Plan-based robot control is described in some more detail in Sect. 14.4. Section 14.5 concludes.

SHAKEY: Experimentation in robot learning and planning (1969)

Author  Peter Hart, Nils Nilsson

Video ID : 704

SRI's robot Shakey (built 1966-1972) was the first mobile robot that could reason about its surroundings. This 1969 movie provides a good look at how Shakey worked.

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 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 (high stiffness)

Author  Centro di Ricerca "E. Piaggio"

Video ID : 472

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

Chapter 56 — Robotics in Agriculture and Forestry

Marcel Bergerman, John Billingsley, John Reid and Eldert van Henten

Robotics for agriculture and forestry (A&F) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.

Ladybird: An intelligent farm robot for the vegetable industry

Author  James Underwood, Calvin Hung, Suchet Bargoti, Mark Calleija, Robert Fitch, Juan Nieto, Salah Sukkarieh

Video ID : 305

This video showcases the Ladybird, an intelligent robot for the vegetable industry. Ladybird provides a flexible platform for sensing and automating commercial vegetable farms. The solar-electric powered vehicle has a flexible drive system that allows precise motion in potentially tight environments, and the platform geometry can be configured to suit different crop configurations. The vehicle autonomously traverses the farm, gathering data from a variety of sensors, including stereo vision, hyperspectral, thermal, and LIDAR. The data is processed to provide useful information for the management and optimization of the crop, including yield mapping, phenotyping, and disease and stress detection. Ladybird is equipped with a manipulator arm for a variety of mechanical tasks, including thinning, weeding (especially of herbicide-resistant weeds), spot spraying, foreign body removal and to support research towards automated harvesting.

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 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: http://www.kipr.org/gcer .