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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.

Safe physical human-robot collaboration

Author  Fabrizio Flacco, Alessandro De Luca

Video ID : 609

The video summarizes the state of the on-going research activities on physical human-robot collaboration (pHRC) at the DIAG Robotics Lab, Sapienza University of Rome, as of March 2013, and performed within the European Research Project FP7 287511 SAPHARI (http://www.saphari.eu) Reference: F. Flacco, A. De Luca: Safe physical human-robot collaboration, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013)

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 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 a parallel robot: Trajectory with load

Author  Maxime Gautier

Video ID : 485

This video shows a trajectory with a known mass payload attached to the platform, used to identify the dynamic parameters and joint drive gains of a parallel prototype robot Orthoglyde. Details and results are given in the paper: S. Briot, M. Gautier: Global identification of joint drive gains and dynamic parameters of parallel robots, Multibody Syst. Dyn. 33(1), 3-26 (2015); doi 10.1007/s11044-013-9403-6

Dynamic identification of Kuka KR270 : Trajectory with load

Author  Maxime Gautier

Video ID : 487

This video shows a trajectory with a known payload mass used to identify the dynamic parameters of the links, load, joint drive gains and gravity compensator of a heavy industrial Kuka KR 270 manipulator Details and results are given in the paper: A. Jubien, M. Gautier, Global identification of spring balancer, dynamic parameters and drive gains of heavy industrial robots, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Tokyo (2013), pp. 1355-1360

Chapter 10 — Redundant Robots

Stefano Chiaverini, Giuseppe Oriolo and Anthony A. Maciejewski

This chapter focuses on redundancy resolution schemes, i. e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes.

In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different firstorder methods to solve kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue when a redundant manipulator is used to execute a repetitive task, e.g., in industrial applications. The use of kinematic redundancy for fault tolerance is analyzed in detail. Suggestions for further reading are given in a final section.

Configuration space control of KUKA Lightweight Robot LWR with EXARM Exoskeleton

Author  Telerobotics Lab

Video ID : 817

This video shows some advanced inverse kinematics mapping that enables the control of a redundant manipulator (KUKA LWR) by means of Cartesian location and geometric correspondence to the human arm. Thereby the null-space of the robot manipulator can be exploited to enable very intuitive operations. Joint limits and singularities are avoided, as well, by optimized mounting of the robot and the hand.

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.

First concentric tube robot teleoperation

Author  Pierre Dupont

Video ID : 250

This 2007 video showcases Brandon Itkowitz's MS thesis work at Boston University. It is the first demonstration of teleoperated control of a concentric tube robot. The robot workspace is the size of a heart chamber. Similar to the child's game "Operation", the user attempts to sequentially touch electrical contacts inside each numbered bead without touching the wire loops surrounding the hole in each bead.

Chapter 40 — Mobility and Manipulation

Oliver Brock, Jaeheung Park and Marc Toussaint

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation,mobilemanipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning formobilemanipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

Flight stability in an aerial redundant manipulator

Author  Christopher Korpela, Matko Orsag, Todd Danko, Bryan Kobe, Clayton McNeil, Robert Pisch, Paul Oh

Video ID : 782

A Buoyancy envelope can be used to compensate for the inherent instability of quadrotor UAVs by decreasing drift and increasing the moment of inertia of the rotorcraft. Also, computer-aided control was implemented and tested for controlling the aerial manipulator using a motion-capture system. The closed-loop controller compensates for the disturbances due to the dynamics of the manipulator and interaction force at the end-effector in the control of the UAV.

Development of a versatile underwater robot - GTS ROV ALPHA

Author  Georgia Tech Savannah Robotics

Video ID : 790

This underwater vehicle won the award for design elegance at the 2009 MATE International ROV competition. In November 2009, it was deployed from the R/V Savannah for an initial sea trial. In the future, it is intended to serve as a platform for underwater manipulation, mapping, and control experiments.

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 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 (2)

Author  Francois Chaumette, Seth Hutchinson, Peter Corke

Video ID : 65

This video shows a 2.5-D VS on a 6 DOF robot arm with (c*^t_c, x_g, theta u_z) as visual features. It corresponds to the results depicted in Figure 34.13.