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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.

Passivity of IPC strategy at 30-Hz sample rate

Author  Stefano Stramigioli

Video ID : 724

In this short video, the effectiveness of the passive sampling approach and IPC control are shown. A "PD" like control is implemented digitally in the classical way and also using IPC and passive sampling. At the used sampling frequency of 30 Hz, it is shown that instability occurs for the standard implementation, but is completely absent in the proposed way.

Chapter 65 — Domestic Robotics

Erwin Prassler, Mario E. Munich, Paolo Pirjanian and Kazuhiro Kosuge

When the first edition of this book was published domestic robots were spoken of as a dream that was slowly becoming reality. At that time, in 2008, we looked back on more than twenty years of research and development in domestic robotics, especially in cleaning robotics. Although everybody expected cleaning to be the killer app for domestic robotics in the first half of these twenty years nothing big really happened. About ten years before the first edition of this book appeared, all of a sudden things started moving. Several small, but also some larger enterprises announced that they would soon launch domestic cleaning robots. The robotics community was anxiously awaiting these first cleaning robots and so were consumers. The big burst, however, was yet to come. The price tag of those cleaning robots was far beyond what people were willing to pay for a vacuum cleaner. It took another four years until, in 2002, a small and inexpensive device, which was not even called a cleaning robot, brought the first breakthrough: Roomba. Sales of the Roomba quickly passed the first million robots and increased rapidly. While for the first years after Roomba’s release, the big players remained on the sidelines, possibly to revise their own designs and, in particular their business models and price tags, some other small players followed quickly and came out with their own products. We reported about theses devices and their creators in the first edition. Since then the momentum in the field of domestics robotics has steadily increased. Nowadays most big appliance manufacturers have domestic cleaning robots in their portfolio. We are not only seeing more and more domestic cleaning robots and lawn mowers on the market, but we are also seeing new types of domestic robots, window cleaners, plant watering robots, tele-presence robots, domestic surveillance robots, and robotic sports devices. Some of these new types of domestic robots are still prototypes or concept studies. Others have already crossed the threshold to becoming commercial products.

For the second edition of this chapter, we have decided to not only enumerate the devices that have emerged and survived in the past five years, but also to take a look back at how it all began, contrasting this retrospection with the burst of progress in the past five years in domestic cleaning robotics. We will not describe and discuss in detail every single cleaning robot that has seen the light of the day, but select those that are representative for the evolution of the technology as well as the market. We will also reserve some space for new types of mobile domestic robots, which will be the success stories or failures for the next edition of this chapter. Further we will look into nonmobile domestic robots, also called smart appliances, and examine their fate. Last but not least, we will look at the recent developments in the area of intelligent homes that surround and, at times, also control the mobile domestic robots and smart appliances described in the preceding sections.

Robotic vacuum cleaners reviewed by Click - Spring 2014

Author  Erwin Prassler

Video ID : 727

Reviews of domestic, vacuum-cleaning robots by BBC.

Chapter 38 — Grasping

Domenico Prattichizzo and Jeffrey C. Trinkle

This chapter introduces fundamental models of grasp analysis. The overall model is a coupling of models that define contact behavior with widely used models of rigid-body kinematics and dynamics. The contact model essentially boils down to the selection of components of contact force and moment that are transmitted through each contact. Mathematical properties of the complete model naturally give rise to five primary grasp types whose physical interpretations provide insight for grasp and manipulation planning.

After introducing the basic models and types of grasps, this chapter focuses on the most important grasp characteristic: complete restraint. A grasp with complete restraint prevents loss of contact and thus is very secure. Two primary restraint properties are form closure and force closure. A form closure grasp guarantees maintenance of contact as long as the links of the hand and the object are well-approximated as rigid and as long as the joint actuators are sufficiently strong. As will be seen, the primary difference between form closure and force closure grasps is the latter’s reliance on contact friction. This translates into requiring fewer contacts to achieve force closure than form closure.

The goal of this chapter is to give a thorough understanding of the all-important grasp properties of form and force closure. This will be done through detailed derivations of grasp models and discussions of illustrative examples. For an indepth historical perspective and a treasure-trove bibliography of papers addressing a wide range of topics in grasping, the reader is referred to [38.1].

Grasp analysis using the MATLAB toolbox SynGrasp

Author  Monica Malvezzi, Guido Gioioso, Gionata Salvietti, Domenico Prattichizzo

Video ID : 551

In this video a examples of few grasp analysis are documented and reported. The analysis is performed using SynGrasp, a MATLAB toolbox for grasp analysis. It provides a graphical user interface (GUI) which the user can adopt to easily load a hand and an object, and a series of functions that the user can assemble and modify to exploit all the toolbox features. The video shows how to use SynGrasp to model and analyze grasping, and, in particular it shows how users can select and load in the GUI a hand model, then choose an object and place it in the workspace selecting its position w.r.t. the hand. The grasp is obtained closing the hand from an initial configuration, which can be set by the users acting on hand joints. Once the grasp is defined, it can be analyzed by evaluating grasp quality measures available in the toolbox. Grasps can be described either using the provided grasp planner or directly defining contact points on the hand with the respective contact normal directions. SynGrasp can model both fully and underactuated robotic hands. An important role in grasp analysis, in particular with underactuated hands, is played by system compliance. SynGrasp can model the stiffness at contact points, at the joints or in the actuation system including transmission. A wide set of analytical functions, continuously increasing with new features and capabilities, has been developed to investigate the main grasp properties: controllable forces and object displacement, manipulability analysis, grasp stiffness and different measures of grasp quality. A set of functions for the graphical representation of the hand, the object, and the main analysis results is provided. The toolbox is freely available at

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 75 — Biologically Inspired Robotics

Fumiya Iida and Auke Jan Ijspeert

Throughout the history of robotics research, nature has been providing numerous ideas and inspirations to robotics engineers. Small insect-like robots, for example, usually make use of reflexive behaviors to avoid obstacles during locomotion, whereas large bipedal robots are designed to control complex human-like leg for climbing up and down stairs. While providing an overview of bio-inspired robotics, this chapter particularly focus on research which aims to employ robotics systems and technologies for our deeper understanding of biological systems. Unlike most of the other robotics research where researchers attempt to develop robotic applications, these types of bio-inspired robots are generally developed to test unsolved hypotheses in biological sciences. Through close collaborations between biologists and roboticists, bio-inspired robotics research contributes not only to elucidating challenging questions in nature but also to developing novel technologies for robotics applications. In this chapter, we first provide a brief historical background of this research area and then an overview of ongoing research methodologies. A few representative case studies will detail the successful instances in which robotics technologies help identifying biological hypotheses. And finally we discuss challenges and perspectives in the field.

Biologically inspired robotics (or bio-inspired robotics in short) is a very broad research area because almost all robotic systems are, in one way or the other, inspired from biological systems. Therefore, there is no clear distinction between bio-inspired robots and the others, and there is no commonly agreed definition [75.1]. For example, legged robots that walk, hop, and run are usually regarded as bio-inspired robots because many biological systems rely on legged locomotion for their survival. On the other hand, many robotics researchers implement biologicalmodels ofmotion control and navigation onto wheeled platforms, which could also be regarded as bio-inspired robots [75.2].

Salamandra Robotica II - Swimming-to-walking transition

Author  Fumiya Iida, Auke Ijspeert

Video ID : 113

This video presents the swimming-to-walking transition of a bioinspired salamander-like robot: Salamandra Robotica II. The modular configuration of this robot takes advantage of coordinated motions of motors based on the rhythmic patterns generated by CPGs. Because of the flexible coordination, the robot is able to exhibit locomotion both underwater and on the ground.

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.

HERMES, a humanoid experimental robot for mobile manipulation and exploration services

Author  Rainer Bischoff

Video ID : 783

Mobile robot HERMES grasps and releases a glass with tactile sensing using joint-angle, encoder values and motor currents. The robot can fill a glass with water from a bottle using vision. It can communicate with natural spoken language,and it can come to you to get your cup and take the cup to the kitchen, by planning a path and avoiding obstacles.

Chapter 49 — Modeling and Control of Wheeled Mobile Robots

Claude Samson, Pascal Morin and Roland Lenain

This chaptermay be seen as a follow up to Chap. 24, devoted to the classification and modeling of basic wheeled mobile robot (WMR) structures, and a natural complement to Chap. 47, which surveys motion planning methods for WMRs. A typical output of these methods is a feasible (or admissible) reference state trajectory for a given mobile robot, and a question which then arises is how to make the physical mobile robot track this reference trajectory via the control of the actuators with which the vehicle is equipped. The object of the present chapter is to bring elements of the answer to this question based on simple and effective control strategies.

The chapter is organized as follows. Section 49.2 is devoted to the choice of controlmodels and the determination of modeling equations associated with the path-following control problem. In Sect. 49.3, the path following and trajectory stabilization problems are addressed in the simplest case when no requirement is made on the robot orientation (i. e., position control). In Sect. 49.4 the same problems are revisited for the control of both position and orientation. The previously mentionned sections consider an ideal robot satisfying the rolling-without-sliding assumption. In Sect. 49.5, we relax this assumption in order to take into account nonideal wheel-ground contact. This is especially important for field-robotics applications and the proposed results are validated through full scale experiments on natural terrain. Finally, a few complementary issues on the feedback control of mobile robots are briefly discussed in the concluding Sect. 49.6, with a list of commented references for further reading on WMRs motion control.

Tracking of an omnidirectional frame with a unicycle-like robot

Author  Guillaume Artus, Pascal Morin, Claude Samson

Video ID : 243

This video shows an experiment performed in 2005 with a unicyle-like robot. A video camera mounted at the top of a robotic arm enabled estimation of the 2-D pose (position/orientation) of the robot with respect to a visual target consisting of three white bars. These bars materialized an omnidirectional moving frame. The experiment demonstrated the capacity of the nonholonomic robot to track in both position and orientation this ominidirectional frame, based on the transverse function control approach.

Chapter 50 — Modeling and Control of Robots on Rough Terrain

Keiji Nagatani, Genya Ishigami and Yoshito Okada

In this chapter, we introduce modeling and control for wheeled mobile robots and tracked vehicles. The target environment is rough terrains, which includes both deformable soil and heaps of rubble. Therefore, the topics are roughly divided into two categories, wheeled robots on deformable soil and tracked vehicles on heaps of rubble.

After providing an overview of this area in Sect. 50.1, a modeling method of wheeled robots on a deformable terrain is introduced in Sect. 50.2. It is based on terramechanics, which is the study focusing on the mechanical properties of natural rough terrain and its response to off-road vehicle, specifically the interaction between wheel/track and soil. In Sect. 50.3, the control of wheeled robots is introduced. A wheeled robot often experiences wheel slippage as well as its sideslip while traversing rough terrain. Therefore, the basic approach in this section is to compensate the slip via steering and driving maneuvers. In the case of navigation on heaps of rubble, tracked vehicles have much advantage. To improve traversability in such challenging environments, some tracked vehicles are equipped with subtracks, and one kinematical modeling method of tracked vehicle on rough terrain is introduced in Sect. 50.4. In addition, stability analysis of such vehicles is introduced in Sect. 50.5. Based on such kinematical model and stability analysis, a sensor-based control of tracked vehicle on rough terrain is introduced in Sect. 50.6. Sect. 50.7 summarizes this chapter.

Qualification testing of a tracked vehicle in the NIST Disaster City

Author  SuperDroid Robots, Inc

Video ID : 189

NIST (National Institute of Standards and Technology) developed a standard test field for evaluation of all-terrain mobile robots, called Disaster City in Texas, U.S.A. The field includes steps, stairs, steep slopes, and random step fields (unfixed wooden blocks), which simulates a disaster environment. This video-clip shows an evaluation test of the tracked vehicle, called LT-F, produced by SuperDroidRobots in 2011 in the Disaster City. All tests had to be performed remotely by the vehicle for 10 successful iterations each to qualify.

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.

PBVS on a 6-DOF robot arm (2)

Author  Francois Chaumette, Seth Hutchinson, Peter Corke

Video ID : 63

This video shows a PBVS on a 6-DOF robot arm with (c*^t_c, theta u) as visual features. It corresponds to the results depicted in Figure 34.10.

Chapter 8 — Motion Control

Wan Kyun Chung, Li-Chen Fu and Torsten Kröger

This chapter will focus on the motion control of robotic rigid manipulators. In other words, this chapter does not treat themotion control ofmobile robots, flexible manipulators, and manipulators with elastic joints. The main challenge in the motion control problem of rigid manipulators is the complexity of their dynamics and uncertainties. The former results from nonlinearity and coupling in the robot manipulators. The latter is twofold: structured and unstructured. Structured uncertainty means imprecise knowledge of the dynamic parameters and will be touched upon in this chapter, whereas unstructured uncertainty results from joint and link flexibility, actuator dynamics, friction, sensor noise, and unknown environment dynamics, and will be treated in other chapters. In this chapter, we begin with an introduction to motion control of robot manipulators from a fundamental viewpoint, followed by a survey and brief review of the relevant advanced materials. Specifically, the dynamic model and useful properties of robot manipulators are recalled in Sect. 8.1. The joint and operational space control approaches, two different viewpoints on control of robot manipulators, are compared in Sect. 8.2. Independent joint control and proportional– integral–derivative (PID) control, widely adopted in the field of industrial robots, are presented in Sects. 8.3 and 8.4, respectively. Tracking control, based on feedback linearization, is introduced in Sect. 8.5. The computed-torque control and its variants are described in Sect. 8.6. Adaptive control is introduced in Sect. 8.7 to solve the problem of structural uncertainty, whereas the optimality and robustness issues are covered in Sect. 8.8. To compute suitable set point signals as input values for these motion controllers, Sect. 8.9 introduces reference trajectory planning concepts. Since most controllers of robotmanipulators are implemented by using microprocessors, the issues of digital implementation are discussed in Sect. 8.10. Finally, learning control, one popular approach to intelligent control, is illustrated in Sect. 8.11.

Gain change of the PID controller

Author  Wan Kyun Chung

Video ID : 25

The control architecture of the PID tracking controller is introduced. Moreover, according to the gain change, the performance variations of the PID controller implemented in the digital control system are shown.