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Chapter 11 — Robots with Flexible Elements

Alessandro De Luca and Wayne J. Book

Design issues, dynamic modeling, trajectory planning, and feedback control problems are presented for robot manipulators having components with mechanical flexibility, either concentrated at the joints or distributed along the links. The chapter is divided accordingly into two main parts. Similarities or differences between the two types of flexibility are pointed out wherever appropriate.

For robots with flexible joints, the dynamic model is derived in detail by following a Lagrangian approach and possible simplified versions are discussed. The problem of computing the nominal torques that produce a desired robot motion is then solved. Regulation and trajectory tracking tasks are addressed by means of linear and nonlinear feedback control designs.

For robots with flexible links, relevant factors that lead to the consideration of distributed flexibility are analyzed. Dynamic models are presented, based on the treatment of flexibility through lumped elements, transfer matrices, or assumed modes. Several specific issues are then highlighted, including the selection of sensors, the model order used for control design, and the generation of effective commands that reduce or eliminate residual vibrations in rest-to-rest maneuvers. Feedback control alternatives are finally discussed.

In each of the two parts of this chapter, a section is devoted to the illustration of the original references and to further readings on the subject.

Rest-to-rest motion for a flexible link

Author  Alessandro De Luca

Video ID : 779

This 2003 video shows a planar one-link flexible arm executing a desired rest-to-rest motion in a given finite time (90 deg in 2 s). Link deformations vanish completely at the desired final time. The applied control law is the combination of a model-based feedforward command designed for a smooth trajectory assigned to the flat output of the system and of a stabilizing PID feedback action on the joint angle around its associated trajectory. References: 1. A. De Luca, G. Di Giovanni: Rest-to-rest motion of a one-link flexible arm, Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatron., Como (2001), pp. 923-928; doi: 10.1109/AIM.2001.936793; 2. A. De Luca, V. Caiano, D. Del Vescovo: Experiments on rest-to-rest motion of a flexible arm, in B. Siciliano, P. Dario (Eds), Experimental Robotics VIII, Springer Tract. Adv. Robot. 5, 338-349 (2003); doi: 10.1007/3-540-36268-1_30

Chapter 51 — Modeling and Control of Underwater Robots

Gianluca Antonelli, Thor I. Fossen and Dana R. Yoerger

This chapter deals with modeling and control of underwater robots. First, a brief introduction showing the constantly expanding role of marine robotics in oceanic engineering is given; this section also contains some historical backgrounds. Most of the following sections strongly overlap with the corresponding chapters presented in this handbook; hence, to avoid useless repetitions, only those aspects peculiar to the underwater environment are discussed, assuming that the reader is already familiar with concepts such as fault detection systems when discussing the corresponding underwater implementation. Themodeling section is presented by focusing on a coefficient-based approach capturing the most relevant underwater dynamic effects. Two sections dealing with the description of the sensor and the actuating systems are then given. Autonomous underwater vehicles require the implementation of mission control system as well as guidance and control algorithms. Underwater localization is also discussed. Underwater manipulation is then briefly approached. Fault detection and fault tolerance, together with the coordination control of multiple underwater vehicles, conclude the theoretical part of the chapter. Two final sections, reporting some successful applications and discussing future perspectives, conclude the chapter. The reader is referred to Chap. 25 for the design issues.

Mariana Trench: HROV Nereus samples the Challenger Deep seafloor

Author  Woods Hole Oceanographic Institution

Video ID : 89

Date: May 31, 2009. Depth: 10,006 meters (6.2 miles). A WHOI-led team successfully brought the newly-built hybrid remotely operated vehicle (HROV) Nereus to the deepest part of the world's ocean, the Challenger Deep in the Pacific Ocean. The dive makes the unmanned Nereus the world's deepest-diving vehicle and the first vehicle to explore the Mariana Trench since 1998. To learn more visit http://www.whoi.edu/page.do?pid=33775.

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.

Deformation-based loop closure for dense RGB-D SLAM

Author  Thomas Whelan

Video ID : 439

This video shows the integration of SLAM-pose-graph optimization, spatially extended KinectFusion, and deformation-based loop closure in dense RGB-D mapping - integrating several of the capabilities discussed in Chap. 46.3.3 and Chap. 46.4, Springer Handbook of Robotics, 2nd edn (2016). Reference: T. Whelan, M. Kaess, H. Johannsson, M. Fallon, J.J. Leonard, J. McDonald: Real-time large scale dense RGB-D SLAM with volumetric fusion, Int. J. Robot. Res. 34(4-5), 598-626 (2014).

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.

Mental-state inference to support human-robot collaboration

Author  Cynthia Breazeal

Video ID : 563

In this video, the Leonardo robot infers mental states from the observable behavior of two human collaborators in order to assist them in achieving their respective goals. The robot engages in a simulation-theory-inspired approach to make these inferences and to plan the appropriate actions to achieve the task goals. Each person wants a different food item (chips or cookies), locked in one of two larger boxes. The robot can operate a remote control interface to open two smaller boxes, one containing chips and the other cookies. The task is inspired by the Sally-Anne false-belief task, where the humans have diverging beliefs caused by a manipulation witnessed by only one of the participants. The robot must keep track of its own beliefs, in addition to inferring the beliefs of the human collaborators, as well as infer their respective goals, to offer the correct assistance.

Chapter 4 — Mechanism and Actuation

Victor Scheinman, J. Michael McCarthy and Jae-Bok Song

This chapter focuses on the principles that guide the design and construction of robotic systems. The kinematics equations and Jacobian of the robot characterize its range of motion and mechanical advantage, and guide the selection of its size and joint arrangement. The tasks a robot is to perform and the associated precision of its movement determine detailed features such as mechanical structure, transmission, and actuator selection. Here we discuss in detail both the mathematical tools and practical considerations that guide the design of mechanisms and actuation for a robot system.

The following sections (Sect. 4.1) discuss characteristics of the mechanisms and actuation that affect the performance of a robot. Sections 4.2–4.6 discuss the basic features of a robot manipulator and their relationship to the mathematical model that is used to characterize its performance. Sections 4.7 and 4.8 focus on the details of the structure and actuation of the robot and how they combine to yield various types of robots. The final Sect. 4.9 relates these design features to various performance metrics.

Harmonic drive

Author  Harmonic Drive AG

Video ID : 649

Fig. 4.28 The harmonic drive.

Chapter 11 — Robots with Flexible Elements

Alessandro De Luca and Wayne J. Book

Design issues, dynamic modeling, trajectory planning, and feedback control problems are presented for robot manipulators having components with mechanical flexibility, either concentrated at the joints or distributed along the links. The chapter is divided accordingly into two main parts. Similarities or differences between the two types of flexibility are pointed out wherever appropriate.

For robots with flexible joints, the dynamic model is derived in detail by following a Lagrangian approach and possible simplified versions are discussed. The problem of computing the nominal torques that produce a desired robot motion is then solved. Regulation and trajectory tracking tasks are addressed by means of linear and nonlinear feedback control designs.

For robots with flexible links, relevant factors that lead to the consideration of distributed flexibility are analyzed. Dynamic models are presented, based on the treatment of flexibility through lumped elements, transfer matrices, or assumed modes. Several specific issues are then highlighted, including the selection of sensors, the model order used for control design, and the generation of effective commands that reduce or eliminate residual vibrations in rest-to-rest maneuvers. Feedback control alternatives are finally discussed.

In each of the two parts of this chapter, a section is devoted to the illustration of the original references and to further readings on the subject.

Feedforward/feedback law for path tracking with a KUKA KR15/2 robot

Author  Michael Thümmel

Video ID : 136

This 2006 video shows the performance of a type of model-based feedforward (using the elastic joint model) plus state-feedback stabilization for trajectory tracking. Designed for an industrial KUKA KR15/2 manipulator having cycloidal gearboxes, which are known for their visco-elasticity, this controller is compared to a standard one for the robot task of moving in a rest-to-rest mode along three (orthogonal) square paths in Cartesian space. References: 1. M. Thümmel: Modellbasierte Regelung mit nichtlinearen inversen Systemen und Beobachtern von Robotern mit elastischen Gelenken, Dissertation, Technische Universität München, Munich, (2006) (in German); 2. A. De Luca, D. Schröder, M. Thümmel: An acceleration-based state observer for robot manipulators with elastic joints, IEEE Int. Conf. Robot. Autom. (ICRA), Rome (2007), pp. 3817-3823, 2007. doi: 10.1109/ROBOT.2007.364064

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 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.

Semi-autonomous teleoperation of multiple UAVs: Tumbling over an obstacle

Author  Antonio Franchi, Paolo Robuffo Giordano

Video ID : 72

This video shows the bilateral teleoperation of a group of four quadrotor UAVs navigating in a cluttered environment. The human operator provides velocity-level motion commands and receives force-feedback information on the UAV interaction with the environment (e.g., presence of obstacles and external disturbances).

Multi-modal, multi-user telepresence and teleaction system

Author  M. Buss, A. Peer, T. Schauss, N. Stefanov, U. Unterhinninghofen, S. Behrendt, G. Farber, J. Leupold, K. Diepold, F. Keyrouz, M. Sarkis, P. Hinterseer, E. Steinbach, B. Farber, H. Pongrac

Video ID : 321

This video shows a multimodal, multiuser telepresence system, consisting of two teleoperated mobile manipulators, each connected to a stationary or a mobile, multimodal, human telerobotic interface. The multimodal-user feedback consists of 3-D vision, 3-D acoustic, and haptic feedback. A cooperative pipe-repair task was performed by the two teleoperated mobile manipulators. Presented at ICRA 2008.

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.

SMErobot - New parallel kinematic with unique concepts for demanding handling and process applications

Author  Martin Haegele

Video ID : 265

Video of demonstrator D1 of SMErobot - The European Robot Initiative for Strengthening the Competitiveness of SMEs in Manufacturing: "New Parallel Kinematic with unique concepts for demanding handling and process applications" SMErobot was an Integrated Project within the 6th Framework Programme of the EC to create a new family of SME-suitable robots and to exploit its potentials for competitive SME manufacturing (March 2005 - May 2009). For more details on the project and this new parallel kinematic, please also watch the "SMErobot video Coffee Break (English)" with Video ID: 261 as well as the "SMErobot Final Project Video" with Video ID: 262 or visit the respective demonstrator website: http://www.smerobot.org/04_demonstrations/#d1