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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 DEOS demonstration mission simulation

Author  Roberto Lampariello, Gerd Hirzinger

Video ID : 339

This video simulation shows an intended task in DLR's DEOS project for grasping an uncooperative, tumbling target satellite (left) by means of a free-flying robot (right, servicer satellite and robot manipulator). The task consists of approaching a predefined point on the target with the robot end-effector, tracking the same point with the robot end-effector while homing in onto it, closing the grasp, and stabilizing the relative motion between the two spacecraft. Following this, the robot performs a berthing task to secure the target in a dedicated docking port on the servicer. The servicer's GNC system is switched off during the entire duration of the grasping maneuver, giving rise to free-floating dynamic behavior of the manipulator. The complete robot trajectory is provided by a motion planner in order to guarantee feasibility with respect to motion constraints, such as the the field of view of the end-effector camera, etc.

Chapter 80 — Roboethics: Social and Ethical Implications

Gianmarco Veruggio, Fiorella Operto and George Bekey

This chapter outlines the main developments of roboethics 9 years after a worldwide debate on the subject – that is, the applied ethics about ethical, legal, and societal aspects of robotics – opened up. Today, roboethics not only counts several thousands of voices on the Web, but is the issue of important literature relating to almost all robotics applications, and of hundreds of rich projects, workshops, and conferences. This increasing interest and sometimes even fierce debate expresses the perception and need of scientists, manufacturers, and users of professional guidelines and ethical indications about robotics in society.

Some of the issues presented in the chapter are well known to engineers, and less known or unknown to scholars of humanities, and vice versa. However, because the subject is transversal to many disciplines, complex, articulated, and often misrepresented, some of the fundamental concepts relating to ethics in science and technology are recalled and clarified.

A detailed taxonomy of sensitive areas is presented. It is based on a study of several years and referred to by scientists and scholars, the result of which is the Euron Roboethics Roadmap. This taxonomy identifies themost evident/urgent/sensitive ethical problems in the main applicative fields of robotics, leaving more in-depth research to further studies.

Roboethics: Military robotics

Author  Fiorella Operto

Video ID : 775

Ethical, legal and societal issues in military robotics. The so-called field of military robotics comprises all the devices resulting from the development of the traditional systems by robotics technology: Integrated defense systems; and A.I. systems for intelligence and surveillance controlling weapons and aircraft capabilities. Unmanned ground vehicles (UGVs), or autonomous tanks: Armored vehicles carrying weapons and/or tactical payloads, intelligent bombs and missiles. UAVs (unmanned aerial vehicles): also referred to as autonomous flying vehicles (AFVs) or drones, unmanned spy planes and remotely piloted bombers. ASV (autonomous surface vessels) and patrol boats. AUVs (autonomous underwater vehicles): Intelligent torpedoes and autonomous submarines. In this field, the main problems could arise from: inadequate management of the unstructured complexity of a hostile scenario; the unpredictability of machine behavior; the increased risk of starting a video-game-like war, due to the decreased perception of its deadly effects; unpredictable side-effects on civilian populations; human-in-control hierarchy and robot’s transparency; psychological issues of humans in robotized environments (mixed teams); accountability and responsibility gap; the assignment of liability for misbehaviors or crimes. Collateral damages: Despite the increasing success of this technology, military hierarchies feel concerned about the potential dangers. Drones can accidentally fall and possibly damage humans and objects. Daily news report about unintended injury or death of innocent non-combatants (usually known as “collateral damage”) from war theaters. Potential friendly-fire casualties in crowded battlefield or due to enemy’s tracking/hijacking.

Chapter 63 — Medical Robotics and Computer-Integrated Surgery

Russell H. Taylor, Arianna Menciassi, Gabor Fichtinger, Paolo Fiorini and Paolo Dario

The growth of medical robotics since the mid- 1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. This chapter will discuss some major themes and illustrate them with examples from current and past research. Further reading providing a more comprehensive review of this rapidly expanding field is suggested in Sect. 63.4.

Medical robotsmay be classified in many ways: by manipulator design (e.g., kinematics, actuation); by level of autonomy (e.g., preprogrammed versus teleoperation versus constrained cooperative control), by targeted anatomy or technique (e.g., cardiac, intravascular, percutaneous, laparoscopic, microsurgical); or intended operating environment (e.g., in-scanner, conventional operating room). In this chapter, we have chosen to focus on the role of medical robots within the context of larger computer-integrated systems including presurgical planning, intraoperative execution, and postoperative assessment and follow-up.

First, we introduce basic concepts of computerintegrated surgery, discuss critical factors affecting the eventual deployment and acceptance of medical robots, and introduce the basic system paradigms of surgical computer-assisted planning, execution, monitoring, and assessment (surgical CAD/CAM) and surgical assistance. In subsequent sections, we provide an overview of the technology ofmedical robot systems and discuss examples of our basic system paradigms, with brief additional discussion topics of remote telesurgery and robotic surgical simulators. We conclude with some thoughts on future research directions and provide suggested further reading.

Variable stiffness manipulator based on layer jamming

Author  MIT/Samsung

Video ID : 832

A tubular, variable-stiffness structure designed for establishing a guide channel for single-port surgery. The thin-layered materials enables jamming stiffness more effectively in a very limited space.

Chapter 36 — Motion for Manipulation Tasks

James Kuffner and Jing Xiao

This chapter serves as an introduction to Part D by giving an overview of motion generation and control strategies in the context of robotic manipulation tasks. Automatic control ranging from the abstract, high-level task specification down to fine-grained feedback at the task interface are considered. Some of the important issues include modeling of the interfaces between the robot and the environment at the different time scales of motion and incorporating sensing and feedback. Manipulation planning is introduced as an extension to the basic motion planning problem, which can be modeled as a hybrid system of continuous configuration spaces arising from the act of grasping and moving parts in the environment. The important example of assembly motion is discussed through the analysis of contact states and compliant motion control. Finally, methods aimed at integrating global planning with state feedback control are summarized.

Learning to place new objects

Author  Yun Jiang et al.

Video ID : 370

The video shows how to a robot learns to place objects stably in preferred locations. Four different tasks are performed: 1) loading a refrigerator, 2) loading a bookshelf, 3) cleaning a table, and 4) loading dish-racks.

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 39 — Cooperative Manipulation

Fabrizio Caccavale and Masaru Uchiyama

This chapter is devoted to cooperative manipulation of a common object by means of two or more robotic arms. The chapter opens with a historical overview of the research on cooperativemanipulation, ranging from early 1970s to very recent years. Kinematics and dynamics of robotic arms cooperatively manipulating a tightly grasped rigid object are presented in depth. As for the kinematics and statics, the chosen approach is based on the socalled symmetric formulation; fundamentals of dynamics and reduced-order models for closed kinematic chains are discussed as well. A few special topics, such as the definition of geometrically meaningful cooperative task space variables, the problem of load distribution, and the definition of manipulability ellipsoids, are included to give the reader a complete picture ofmodeling and evaluation methodologies for cooperative manipulators. Then, the chapter presents the main strategies for controlling both the motion of the cooperative system and the interaction forces between the manipulators and the grasped object; in detail, fundamentals of hybrid force/position control, proportional–derivative (PD)-type force/position control schemes, feedback linearization techniques, and impedance control approaches are given. In the last section further reading on advanced topics related to control of cooperative robots is suggested; in detail, advanced nonlinear control strategies are briefly discussed (i. e., intelligent control approaches, synchronization control, decentralized control); also, fundamental results on modeling and control of cooperative systems possessing some degree of flexibility are briefly outlined.

Cooperative capturing via flexible manipulators

Author  Masaru Uchiyama

Video ID : 68

This is a video showing cooperative capturing of a spinning object via flexible manipulators. Reference: T. Miyabe, M. Yamano, A. Konno, M. Uchiyama: An approach towards a robust object recovery with flexible manipulators, Proc. IEEE/RSJ Int. Conf. Intel. Robot. Syst. (2001) pp. 907-912.

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 D1 assembly with dual-arm industrial manipulators

Author  Martin Haegele, Thilo Zimmermann, Björn Kahl

Video ID : 380

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: Fenceless approach in a safe; environment & Gesture Control (00:27); Chapter 3: Cooperative motion (00:57); Chapter 4: Minimal fixtures for maximum flexibility (Scan Objects) (01:36); Chapter 5: Offline preview (02:12); Chapter 6: Task execution (02:26); Chapter 7: Tool changer device (03:49); Chapter 8: Statement (04:11); Chapter 9: Outro (04:39); Chapter 10: The Consortium (05:08). For details, please visit: http://www.smerobotics.org/project/video-of-demonstrator-d1.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.

Introduction to evolutionary robotics at EPFL

Author  Dario Floreano

Video ID : 119

Method for evolving the neural network of a robot. Valid gene sequences are extracted (magnifying lens) from a binary string representing the genome of the robot. Those genes are translated into neurons of different types (colors) according to the genetic specifications, such as sensory, motor, excitatory, or inhibitory neurons. The corresponding neural network is connected to the sensors and motors of the robot and the resulting behavior of the robot is measured according to the fitness function. The genomes of the individuals that had the worst performance are discarded from the population (symbolically thrown into a dustbin) whereas the genomes of the best individuals are paired and crossed over with small random mutations to generate new offspring (the process of selective reproduction is symbolically shown to occur in a mother robot). After several generations of selective reproductions with mutations, robots display better or novel behaviors.

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 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.