View Chapter

Chapter 30 — Sonar Sensing

Lindsay Kleeman and Roman Kuc

Sonar or ultrasonic sensing uses the propagation of acoustic energy at higher frequencies than normal hearing to extract information from the environment. This chapter presents the fundamentals and physics of sonar sensing for object localization, landmark measurement and classification in robotics applications. The source of sonar artifacts is explained and how they can be dealt with. Different ultrasonic transducer technologies are outlined with their main characteristics highlighted.

Sonar systems are described that range in sophistication from low-cost threshold-based ranging modules to multitransducer multipulse configurations with associated signal processing requirements capable of accurate range and bearing measurement, interference rejection, motion compensation, and target classification. Continuous-transmission frequency-modulated (CTFM) systems are introduced and their ability to improve target sensitivity in the presence of noise is discussed. Various sonar ring designs that provide rapid surrounding environmental coverage are described in conjunction with mapping results. Finally the chapter ends with a discussion of biomimetic sonar, which draws inspiration from animals such as bats and dolphins.

Monash DSP sonar tracking a moving plane

Author  Lindsay Kleeman

Video ID : 313

A four-transducer system is controlled with a DSP microcontroller which processes echoes to determine the normal incidence and range to a plane reflector. The transducer scans to locate the plane and then tracks the normal-incidence section of the plane as it moves in real time.

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.

Raytheon Sarcos exoskeleton

Author  Sarcos

Video ID : 646

Fig. 4.22b Applications of hydraulic actuators to robot: Sarcos exoskeleton (Raytheon).

Chapter 12 — Robotic Systems Architectures and Programming

David Kortenkamp, Reid Simmons and Davide Brugali

Robot software systems tend to be complex. This complexity is due, in large part, to the need to control diverse sensors and actuators in real time, in the face of significant uncertainty and noise. Robot systems must work to achieve tasks while monitoring for, and reacting to, unexpected situations. Doing all this concurrently and asynchronously adds immensely to system complexity.

The use of a well-conceived architecture, together with programming tools that support the architecture, can often help to manage that complexity. Currently, there is no single architecture that is best for all applications – different architectures have different advantages and disadvantages. It is important to understand those strengths and weaknesses when choosing an architectural approach for a given application.

This chapter presents various approaches to architecting robotic systems. It starts by defining terms and setting the context, including a recounting of the historical developments in the area of robot architectures. The chapter then discusses in more depth the major types of architectural components in use today – behavioral control (Chap. 13), executives, and task planners (Chap. 14) – along with commonly used techniques for interconnecting connecting those components. Throughout, emphasis will be placed on programming tools and environments that support these architectures. A case study is then presented, followed by a brief discussion of further reading.

Software product line engineering for robotics

Author  Davide Brugali

Video ID : 273

The video illustrates the software product-line approach to the development of robot software control systems and the open source HyperFlex toolchain that supports it.

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.

Gravity‐independent rock‐climbing robot and a sample acquisition tool with microspine grippers

Author  Aaron Parness, Matthew Frost, Nitish Thatte, Jonathan P King, Kevin Witkoe, Moises Nevarez, Michael Garrett, Hrand Aghazarian, Brett Kennedy

Video ID : 414

NASA JPL researchers present a 250 mm diameter omni-directional anchor that uses an array of claws with suspension flexures, called microspines, designed to grip rocks on the surfaces of asteroids and comets and to grip the cliff faces and lava tubes of Mars. Part of the paper: A. Parness, M. Frost, N. Thatte, J.P. King: Gravity-independent mobility and drilling on natural rock using microspines, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), St. Paul (2012), pp. 3437-3442.

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.

Exploitation of environmental constraints in human and robotic grasping

Author  Clemens Eppner, Raphael Deimel, Jose Alvarez-Ruiz, Marianne Maertens, Oliver Brock

Video ID : 657

We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been established. We present evidence for this view by showing robust robotic grasping based on constraint-exploiting grasp strategies, and we show that it is possible to design robotic hands with inherent capabilities for the exploitation of environmental constraints.

Chapter 51 — Modeling and Control of Underwater Robots

Gianluca Antonelli, Thor I. Fossen and Dana R. Yoerger

This chapter deals with modeling and control of underwater robots. First, a brief introduction showing the constantly expanding role of marine robotics in oceanic engineering is given; this section also contains some historical backgrounds. Most of the following sections strongly overlap with the corresponding chapters presented in this handbook; hence, to avoid useless repetitions, only those aspects peculiar to the underwater environment are discussed, assuming that the reader is already familiar with concepts such as fault detection systems when discussing the corresponding underwater implementation. Themodeling section is presented by focusing on a coefficient-based approach capturing the most relevant underwater dynamic effects. Two sections dealing with the description of the sensor and the actuating systems are then given. Autonomous underwater vehicles require the implementation of mission control system as well as guidance and control algorithms. Underwater localization is also discussed. Underwater manipulation is then briefly approached. Fault detection and fault tolerance, together with the coordination control of multiple underwater vehicles, conclude the theoretical part of the chapter. Two final sections, reporting some successful applications and discussing future perspectives, conclude the chapter. The reader is referred to Chap. 25 for the design issues.

Mariana Trench: HROV Nereus samples the Challenger Deep seafloor

Author  Woods Hole Oceanographic Institution

Video ID : 89

Date: May 31, 2009. Depth: 10,006 meters (6.2 miles). A WHOI-led team successfully brought the newly-built hybrid remotely operated vehicle (HROV) Nereus to the deepest part of the world's ocean, the Challenger Deep in the Pacific Ocean. The dive makes the unmanned Nereus the world's deepest-diving vehicle and the first vehicle to explore the Mariana Trench since 1998. To learn more visit http://www.whoi.edu/page.do?pid=33775.

Chapter 41 — Active Manipulation for Perception

Anna Petrovskaya and Kaijen Hsiao

This chapter covers perceptual methods in which manipulation is an integral part of perception. These methods face special challenges due to data sparsity and high costs of sensing actions. However, they can also succeed where other perceptual methods fail, for example, in poor-visibility conditions or for learning the physical properties of a scene.

The chapter focuses on specialized methods that have been developed for object localization, inference, planning, recognition, and modeling in activemanipulation approaches.We concludewith a discussion of real-life applications and directions for future research.

Tactile exploration and modeling using shape primitives

Author  Francesco Mazzini

Video ID : 76

This video shows a robot performing tactile exploration and modeling of a lab-constructed scene that was designed to be similar to those found in interventions for underwater oil spills (leaking pipe). Representing the scene with geometric primitives enables the surface to be described using only sparse tactile data from joint encoders. The robot's movements are chosen to maximize the expected increase in knowledge about the scene.

Chapter 22 — Modular Robots

I-Ming Chen and Mark Yim

This chapter presents a discussion of modular robots from both an industrial and a research point of view. The chapter is divided into four sections, one focusing on existing reconfigurable modular manipulators typically in an industry setting (Sect. 22.2) and another focusing on self-reconfigurable modular robots typically in a research setting (Sect. 22.4). Both sections are sandwiched between the introduction and conclusion sections.

This chapter is focused on design issues. Rather than a survey of existing systems, it presents some of the existing systems in the context of a discussion of the issues and elements in industrial modular robotics and modular robotics research. The reader is encouraged to look at the references for further discussion on any of the presented topics.

SMORES

Author  Jay Davey

Video ID : 1

SMORES robot showing self-reconfiguration. Reference: J. Davey, N. Kwok, M. Yim: Emulating self-reconfigurable robots - Design of the SMORES system, Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS), Vilamoura (2012), pp. 4464-4469

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.

Motor-skill learning for robotics

Author  Jan Peters, Jens Kober, Katharina Mülling

Video ID : 667

We propose to divide the generic skill-learning problem into parts that can be well-understood from a robotics point of view. After appropriate learning approaches have been designed for these basic components, they will serve as the ingredients of a general approach to robot-skill learning. This video shows results of our work on learning to control, learning elementary movements, as well as steps towards the learning of complex tasks.

Chapter 41 — Active Manipulation for Perception

Anna Petrovskaya and Kaijen Hsiao

This chapter covers perceptual methods in which manipulation is an integral part of perception. These methods face special challenges due to data sparsity and high costs of sensing actions. However, they can also succeed where other perceptual methods fail, for example, in poor-visibility conditions or for learning the physical properties of a scene.

The chapter focuses on specialized methods that have been developed for object localization, inference, planning, recognition, and modeling in activemanipulation approaches.We concludewith a discussion of real-life applications and directions for future research.

Modeling articulated objects using active manipulation

Author  Juergen Strum

Video ID : 78

The video illustrates a mobile, manipulation robot that interacts with various articulated objects, such as a fridge and a dishwasher, in a kitchen environment. During interaction, the robot learns their kinematic properties such as the rotation axis and the configuration space. Knowing the kinematic model of these objects improves the performance of the robot and enables motion planning. Service robots operating in domestic environments are typically faced with a variety of objects they have to deal with to fulfill their tasks. Some of these objects are articulated such as cabinet doors and drawers, or room and garage doors. The ability to deal with such articulated objects is relevant for service robots, as, for example, they need to open doors when navigating between rooms and to open cabinets to pick up objects in fetch-and-carry applications. We developed a complete probabilistic framework that enables robots to learn the kinematic models of articulated objects from observations of their motion. We combine parametric and nonparametric models consistently and utilize the advantages of both methods. As a result of our approach, a robot can robustly operate articulated objects in unstructured environments. All software is available open-source (including documentation and tutorials) on http://www.ros.org/wiki/articulation.