View Chapter

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.

Bayesian Embedded Perception in Inria/Toyota instrumented platform

Author  Christian Laugier, E-Motion Team

Video ID : 566

This video illustrates the concept of “Embedded Bayesian Perception”, which has been developed by Inria and implemented on the Inria/Toyota experimental Lexus vehicle. The objective is to improve the robustness of the on-board perception system of the vehicle, by appropriately fusing the data provided by several heterogeneous sensors. The system has been developed as a key component of an electronic co-pilot, designed for the purpose of detecting dangerous driving situations a few seconds ahead. The approach relies on the concept of the “Bayesian Occupancy Filter” developed by the Inria E-Motion Team. More technical details can be found in [62.25].

Chapter 37 — Contact Modeling and Manipulation

Imin Kao, Kevin M. Lynch and Joel W. Burdick

Robotic manipulators use contact forces to grasp and manipulate objects in their environments. Fixtures rely on contacts to immobilize workpieces. Mobile robots and humanoids use wheels or feet to generate the contact forces that allow them to locomote. Modeling of the contact interface, therefore, is fundamental to analysis, design, planning, and control of many robotic tasks.

This chapter presents an overview of the modeling of contact interfaces, with a particular focus on their use in manipulation tasks, including graspless or nonprehensile manipulation modes such as pushing. Analysis and design of grasps and fixtures also depends on contact modeling, and these are discussed in more detail in Chap. 38. Sections 37.2–37.5 focus on rigid-body models of contact. Section 37.2 describes the kinematic constraints caused by contact, and Sect. 37.3 describes the contact forces that may arise with Coulomb friction. Section 37.4 provides examples of analysis of multicontact manipulation tasks with rigid bodies and Coulomb friction. Section 37.5 extends the analysis to manipulation by pushing. Section 37.6 introduces modeling of contact interfaces, kinematic duality, and pressure distribution and soft contact interface. Section 37.7 describes the concept of the friction limit surface and illustrates it with an example demonstrating the construction of a limit surface for a soft contact. Finally, Sect. 37.8 discusses how these more accurate models can be used in fixture analysis and design.

Pushing, sliding, and toppling

Author  Kevin Lynch

Video ID : 802

This video demonstrates sliding or toppling of a pushed object depending on the support friction coefficient, the object's center of mass location, and the pushing force, as illustrated in Figure 37.8.

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 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.

Snake robot for surgery in tight spaces

Author  Vanderbilt University - Prof. Nabil Simaan

Video ID : 830

This video shows rotation about the backbone of a snake robot in order to achieve suturing in tight spaces. The work on actuation compensation for continuum robots was done by Nabil Simaan and Kai Xu. The movie shows how the robot behaves with and without actuation compensation.

Chapter 21 — Actuators for Soft Robotics

Alin Albu-Schäffer and Antonio Bicchi

Although we do not know as yet how robots of the future will look like exactly, most of us are sure that they will not resemble the heavy, bulky, rigid machines dangerously moving around in old fashioned industrial automation. There is a growing consensus, in the research community as well as in expectations from the public, that robots of the next generation will be physically compliant and adaptable machines, closely interacting with humans and moving safely, smoothly and efficiently - in other terms, robots will be soft.

This chapter discusses the design, modeling and control of actuators for the new generation of soft robots, which can replace conventional actuators in applications where rigidity is not the first and foremost concern in performance. The chapter focuses on the technology, modeling, and control of lumped parameters of soft robotics, that is, systems of discrete, interconnected, and compliant elements. Distributed parameters, snakelike and continuum soft robotics, are presented in Chap. 20, while Chap. 23 discusses in detail the biomimetic motivations that are often behind soft robotics.

DLR Hand Arm System throwing a ball and Justin catching it

Author  Alin Albu-Schäffer, Thomas Bahls, Berthold Bäuml, Maxime Chalon, Markus Grebenstein, Oliver Eiberger, Werner Friedl, Hannes Höppner, Dominic Lakatos, Nico Mansfeld, Florian Petit, Jens Reinecke, Roman Weitschat, Sebastian Wolf, Tilo Wüsthoff

Video ID : 547

The DLR Hand Arm System throws a ball and Justin catches it. There is no data connection between the two systems. Justin catches the ball by visual observation.

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.

Google's Project Tango

Author  Google, Inc.

Video ID : 120

Google's Project Tango has been collaborating with robotics laboratories from around the world to synthesize the past decade of research and computer vision into the development of a new class of mobile devices. This video contains one of the first public announcements and presentations of a device that can be used for multiple robot-perception applications described in this chapter.

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.

One-dimensional binary manipulator

Author  Greg Chirikjian

Video ID : 159

Greg Chirikjian's binary manipulator operating in one dimension.

Chapter 13 — Behavior-Based Systems

François Michaud and Monica Nicolescu

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creature’s ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to explain behavior-based systems and their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 13.3. Section 13.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multirobot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

Experience-based learning of high-level task representations: Reproduction (2)

Author  Monica Nicolescu

Video ID : 31

This is a video recorded in early 2000s, showing a Pioneer robot learning to visit a number of targets in a certain order - the robot execution stage. The robot training stage is also shown in a related video in this chapter. References: 1. M. Nicolescu, M.J. Mataric: Experience-based learning of task representations from human-robot interaction, Proc. IEEE Int. Symp. Comput. Intell. Robot. Autom. , Banff (2001), pp. 463-468; 2. M. Nicolescu, M.J. Mataric: Learning and interacting in human-robot domains, IEEE Trans. Syst. Man Cybernet. A31(5), 419-430 (2001)

Chapter 28 — Force and Tactile Sensing

Mark R. Cutkosky and William Provancher

This chapter provides an overview of force and tactile sensing, with the primary emphasis placed on tactile sensing. We begin by presenting some basic considerations in choosing a tactile sensor and then review a wide variety of sensor types, including proximity, kinematic, force, dynamic, contact, skin deflection, thermal, and pressure sensors. We also review various transduction methods, appropriate for each general sensor type. We consider the information that these various types of sensors provide in terms of whether they are most useful for manipulation, surface exploration or being responsive to contacts from external agents.

Concerning the interpretation of tactile information, we describe the general problems and present two short illustrative examples. The first involves intrinsic tactile sensing, i. e., estimating contact locations and forces from force sensors. The second involves contact pressure sensing, i. e., estimating surface normal and shear stress distributions from an array of sensors in an elastic skin. We conclude with a brief discussion of the challenges that remain to be solved in packaging and manufacturing damage-tolerant tactile sensors.

The effect of twice dropping, and then gently placing, a two-gram weight on a small capacitive tactile array

Author  Mark Cutkosky

Video ID : 15

Video illustrating the effect of twice dropping, and then gently placing, a two-gram weight on a small capacitive tactile array sampled at 20 Hz. The first drop produces a large dynamic signal in comparison to the static load, but the second drop is missed, demonstrating the value of having dynamic tactile sensing.

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 on

Author  Alin Albu-Schaeffer

Video ID : 134

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 is active in the controller. The transient response to a contact force (a human pushing on the end-effector) is very short and free of oscillations. This is one of two coordinated videos, the other being for the case with controller damping turned off. 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