View Chapter

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.

Modsnake climbing a tree

Author  Howie Choset

Video ID : 168

The CMU Modsnake climbing a tree and surveying an area from this high vantage point.

Chapter 58 — Robotics in Hazardous Applications

James Trevelyan, William R. Hamel and Sung-Chul Kang

Robotics researchers have worked hard to realize a long-awaited vision: machines that can eliminate the need for people to work in hazardous environments. Chapter 60 is framed by the vision of disaster response: search and rescue robots carrying people from burning buildings or tunneling through collapsed rock falls to reach trapped miners. In this chapter we review tangible progress towards robots that perform routine work in places too dangerous for humans. Researchers still have many challenges ahead of them but there has been remarkable progress in some areas. Hazardous environments present special challenges for the accomplishment of desired tasks depending on the nature and magnitude of the hazards. Hazards may be present in the form of radiation, toxic contamination, falling objects or potential explosions. Technology that specialized engineering companies can develop and sell without active help from researchers marks the frontier of commercial feasibility. Just inside this border lie teleoperated robots for explosive ordnance disposal (EOD) and for underwater engineering work. Even with the typical tenfold disadvantage in manipulation performance imposed by the limits of today’s telepresence and teleoperation technology, in terms of human dexterity and speed, robots often can offer a more cost-effective solution. However, most routine applications in hazardous environments still lie far beyond the feasibility frontier. Fire fighting, remediating nuclear contamination, reactor decommissioning, tunneling, underwater engineering, underground mining and clearance of landmines and unexploded ordnance still present many unsolved problems.

Remote-control heavy equipment used in debris removal at Fukushima reactor 3 (9/22/2012)

Author  James P. Trevelyan

Video ID : 578

A brief clip showing actual operation of a remote-control cutting equipment for dismantling the roof structure at one of the three ruined reactor buildings at the Fukushima Daiichi power plant.

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.

The handbook — The story continues

Author  Bruno Siciliano

Video ID : 845

This video illustrates the joyful mood of the big team of the Springer Handbook of Robotics at the completion of the Second Edition.

Chapter 41 — Active Manipulation for Perception

Anna Petrovskaya and Kaijen Hsiao

This chapter covers perceptual methods in which manipulation is an integral part of perception. These methods face special challenges due to data sparsity and high costs of sensing actions. However, they can also succeed where other perceptual methods fail, for example, in poor-visibility conditions or for learning the physical properties of a scene.

The chapter focuses on specialized methods that have been developed for object localization, inference, planning, recognition, and modeling in activemanipulation approaches.We concludewith a discussion of real-life applications and directions for future research.

Tactile exploration and modeling using shape primitives

Author  Francesco Mazzini

Video ID : 76

This video shows a robot performing tactile exploration and modeling of a lab-constructed scene that was designed to be similar to those found in interventions for underwater oil spills (leaking pipe). Representing the scene with geometric primitives enables the surface to be described using only sparse tactile data from joint encoders. The robot's movements are chosen to maximize the expected increase in knowledge about the scene.

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.

HandSOME exoskeleton

Author  Peter Lum

Video ID : 568

A stroke patient's ability to pick up objects is immediately improved after donning the HandSOME orthosis. Springs provide a customized assistance profile that increases the active range of motion with only minimal decreases in grip force.

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.

Cybernetic human HRP-4C walking

Author  AIST

Video ID : 524

A humanoid robot with human-like dimensions developed by AIST.

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 learning applied to task execution

Author  Cynthia Breazeal

Video ID : 562

This is a video demonstration of the Leonardo robot integrating learning via tutelage, self motivated learning and preference learning to perform a tangram-like task. First the robot learns a policy for how to operate a remote-control box to reveal key shapes needed for the next task, integrating self-motivated exploration with tutelage. The human can shape what the robot learns through a variety of social means. Once Leo has learned a policy, the robot begins the tangram task, which is to make a sailboat figure out of the colored blocks on the virtual workspace. During this interaction, the person has a preference for which block colors to use (yellow and blue), which he conveys through nonverbal means. The robot learns this preference rule from observing these nonverbal cues. During the task, the robot needs blocks of a certain shape and color and which are not readily available on the workspace, but can be accessed by operating the remote-control box to reveal those shapes. Leo evokes those recently learned policies to access those shapes to achieve the goal of making the sailboat figure.

Chapter 53 — Multiple Mobile Robot Systems

Lynne E. Parker, Daniela Rus and Gaurav S. Sukhatme

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.

Elements of cooperative behavior in autonomous mobile robots

Author  David Jung, Gordon Cheng, Alexander Zelinsky

Video ID : 200

Two robots are used to demonstrate cooperative behavior with the application of cleaning. One robot sweeps particles along a wall into a pile, and the other robot uses a vacuum to clean up the pile. The robot with the vacuum tracks the location of the sweeping robot to find where the pile of particles has been left.

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 with load

Author  Maxime Gautier

Video ID : 483

This video shows a trajectory with a known payload mass of 4.6 (kg) used to identify the dynamic parameters and torque-sensor gains 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, preprints 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 35 — Multisensor Data Fusion

Hugh Durrant-Whyte and Thomas C. Henderson

Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization.

This chapter has three parts: methods, architectures, and applications. Most current data fusion methods employ probabilistic descriptions of observations and processes and use Bayes’ rule to combine this information. This chapter surveys the main probabilistic modeling and fusion techniques including grid-based models, Kalman filtering, and sequential Monte Carlo techniques. This chapter also briefly reviews a number of nonprobabilistic data fusion methods. Data fusion systems are often complex combinations of sensor devices, processing, and fusion algorithms. This chapter provides an overview of key principles in data fusion architectures from both a hardware and algorithmic viewpoint. The applications of data fusion are pervasive in robotics and underly the core problem of sensing, estimation, and perception. We highlight two example applications that bring out these features. The first describes a navigation or self-tracking application for an autonomous vehicle. The second describes an application in mapping and environment modeling.

The essential algorithmic tools of data fusion are reasonably well established. However, the development and use of these tools in realistic robotics applications is still developing.

Application of visual odometry for sewer-inspection robots

Author  José Saenz, Christoph Walter, Erik Schulenburg, Norbert Elkmann, Heiko Althoff

Video ID : 638

Exploits a multisensor robot (multiple cameras and range finder) to inspect pipelines.