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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.

Reducing uncertainty in robotics surface-assembly tasks

Author  Jing Xiao et al.

Video ID : 356

This video demonstrates how surface assembly strategies with pose estimation can be used to overcome pose uncertainties. The assembly path is updated based on the newly estimated values of parameters after the compliant exploratory move. In this way, the robot is able to successfully overcome disparities between the nominal and the actual poses of the objects to accomplish the assembly. No force sensor is used.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

An assistive, decision-and-control architecture for force-sensitive, hand-arm systems driven via human-machine interfaces (MM1)

Author  Jörn Vogel, Sami Haddadin, John D. Simeral, Daniel Bacher , Beata Jarosiewicz, Leigh R. Hochberg, John P. Donoghue, Patrick van der Smagt

Video ID : 619

The video shows the "grasp" and "release" skills demonstrated in a 1-D control task using the Braingate2 neural-interface system. The robot is controlled through a multipriority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this, a decision-and-control architecture, which uses sensory information available from the robotic system to evaluate the current state of task execution, is employed.

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.

Hierarchical optimization for pose graphs on manifolds

Author  Giorgio Grisetti

Video ID : 445

This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the HOGMAN algorithm. Reference: G. Grisetti, R. Kuemmerle, C. Stachniss, U. Frese, C. Hertzberg: Hierarchical optimization on manifolds for online 2-D and 3-D mapping, IEEE Int. Conf. Robot. Autom. (ICRA), Anchorage (2010), pp. 273-278; doi: 10.1109/ROBOT.2010.5509407.

Chapter 23 — Biomimetic Robots

Kyu-Jin Cho and Robert Wood

Biomimetic robot designs attempt to translate biological principles into engineered systems, replacing more classical engineering solutions in order to achieve a function observed in the natural system. This chapter will focus on mechanism design for bio-inspired robots that replicate key principles from nature with novel engineering solutions. The challenges of biomimetic design include developing a deep understanding of the relevant natural system and translating this understanding into engineering design rules. This often entails the development of novel fabrication and actuation to realize the biomimetic design.

This chapter consists of four sections. In Sect. 23.1, we will define what biomimetic design entails, and contrast biomimetic robots with bio-inspired robots. In Sect. 23.2, we will discuss the fundamental components for developing a biomimetic robot. In Sect. 23.3, we will review detailed biomimetic designs that have been developed for canonical robot locomotion behaviors including flapping-wing flight, jumping, crawling, wall climbing, and swimming. In Sect. 23.4, we will discuss the enabling technologies for these biomimetic designs including material and fabrication.

A single-motor-actuated, miniature, steerable jumping robot

Author  Jianguo Zhao, Jing Xu, Bingtuan Gao, Ning Xi, Fernando J. Cintron, Matt W. Mutka, Li Xiao

Video ID : 280

The contents of the video are divided into three parts. The first part illustrates the individual functions of the robot such as jumping, self-righting and steering. The second part demonstrates the robot's locomotion capability in indoor environments. Scenarios such as jumping from the floor, jumping in an office and jumping over stairs are included. The third part shows the robot's locomotion capability in outdoor environments. Experiments on uneven ground, ground with small gravels and ground with grass are included.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

Torque control for teaching peg-in-hole via physical human-robot interaction

Author  Alin-Albu Schäffer

Video ID : 627

Teaching by demonstration is a typical application for impedance controllers. A practical demonstration was given with the task of teaching for automatic insertion of a piston into a motor block. Teaching is realized by guiding the robot with the human hand. It was initially known that the axes of the holes in the motor block were vertically oriented. In the teaching phase, high stiffness components for the orientations were commanded (150 Nm/rad), while the translational stiffness was set to zero. This allowed only translational movements to be demonstrated by the human operator. In the second phase, the taught trajectory has been automatically reproduced by the robot. In this phase, high values were assigned for the translational stiffness (3000 N/m), while the stiffness for the rotations was low (60 Nm/rad). This enabled the robot to compensate for the remaining position errors. For two pistons, the total time for the assembly was 6 s. In this experiment, the assembly was executed automatically four-times faster than by the human operator holding the robot as an input device in the teaching phase (24 s), while the free-hand execution of the task by a human requires about 4 s.

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.

A robot that forms a good spatial formation

Author  Takayuki Kanda

Video ID : 257

The video illustrates one of capabilities of social robots developed for making interaction with people smooth and natural. With the developed technique, the robot has the capability to detect the attention of the user based on his location and to adjust its standing position so that it forms a good spatial formation, in which they can easily talk about the object of their attention. In the video, when the user looks around for the computers in a room, the robot moves to a location where it is convenient to explain the computers.

Chapter 47 — Motion Planning and Obstacle Avoidance

Javier Minguez, Florant Lamiraux and Jean-Paul Laumond

This chapter describes motion planning and obstacle avoidance for mobile robots. We will see how the two areas do not share the same modeling background. From the very beginning of motion planning, research has been dominated by computer sciences. Researchers aim at devising well-grounded algorithms with well-understood completeness and exactness properties.

The challenge of this chapter is to present both nonholonomic motion planning (Sects. 47.1–47.6) and obstacle avoidance (Sects. 47.7–47.10) issues. Section 47.11 reviews recent successful approaches that tend to embrace the whole problemofmotion planning and motion control. These approaches benefit from both nonholonomic motion planning and obstacle avoidance methods.

Autonomous robot cars drive in the DARPA Urban Challenge

Author  GovernmentTechnology

Video ID : 714

In order to forster research and development in the domain of autonomous navigation, the DARPA agency organized a challenge in 2007 for competitors to develop autonomous vehicles which are able to follow an itinerary through an urban environment. Navigation within unstructured areas like parking lots made extensive use of RRT-like methods.

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.

Cartesian impedance control with damping off

Author  Alin Albu-Schaeffer

Video ID : 133

This 2010 video shows the performance of a Cartesian impedance controller for the torque-controlled KUKA-LWR robot holding an extra payload, when the damping term in the controller has been turned off. The response to a contact force (a human pushing on the end-effector) is oscillatory due to the joint elasticity. This is one of two coordinated videos, the other for the case with controller damping turned on. Reference: A. Albu-Schaeffer, C. Ott, G. Hirzinger: A unified passivity-based control framework for position, torque and impedance control of flexible joint robots, Int. J. Robot. Res. 26(1), 23-39 (2007) doi: 10.1177/0278364907073776

Chapter 62 — Intelligent Vehicles

Alberto Broggi, Alex Zelinsky, Ümit Özgüner and Christian Laugier

This chapter describes the emerging robotics application field of intelligent vehicles – motor vehicles that have autonomous functions and capabilities. The chapter is organized as follows. Section 62.1 provides a motivation for why the development of intelligent vehicles is important, a brief history of the field, and the potential benefits of the technology. Section 62.2 describes the technologies that enable intelligent vehicles to sense vehicle, environment, and driver state, work with digital maps and satellite navigation, and communicate with intelligent transportation infrastructure. Section 62.3 describes the challenges and solutions associated with road scene understanding – a key capability for all intelligent vehicles. Section 62.4 describes advanced driver assistance systems, which use the robotics and sensing technologies described earlier to create new safety and convenience systems for motor vehicles, such as collision avoidance, lane keeping, and parking assistance. Section 62.5 describes driver monitoring technologies that are being developed to mitigate driver fatigue, inattention, and impairment. Section 62.6 describes fully autonomous intelligent vehicles systems that have been developed and deployed. The chapter is concluded in Sect. 62.7 with a discussion of future prospects, while Sect. 62.8 provides references to further reading and additional resources.

Cybercars and the city of tomorrow

Author  Christian Laugier, Michel Parent, Inria Multimedia

Video ID : 429

The video presents an overview of the CityMobil European Project and of the related concept of cybercars.

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 D3 assembly with sensitive compliant robot arms

Author  Martin Haegele, Thilo Zimmermann, Björn Kahl

Video ID : 382

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: Work cell description and configuration (00:29); Chapter 3: Selection of the job (00:50); Chapter 4: Preparation step (01:09); Chapter 5: Riveting (01:44); Chapter 6: Error handling with automatic solution (02:17); Chapter 7: Finalise workflow (02:34); Chapter 8: Statement (03:09); Chapter 9: Outro (03:40); Chapter 10: The Consortium (03:54). For details, please visit: http://www.smerobotics.org/project/video-of-demonstrator-d3.html