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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.

Handle localization and grasping

Author  Robert Platt

Video ID : 652

The robot localizes and grasps appropriate handles on novel objects in real time.

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.

PROUD2013 - Inside VisLab's driverless car

Author  Alberto Broggi

Video ID : 178

This video shows the internal and external view of what happened during the PROUD2013 driverlesscar test in downtown Parma, Italy, on July 12, 2013. It also displays the internal status of the vehicle plus some vehicle data (speed, steering angle, and some perception results like pedestrian detection, roundabout merging alert, freeway merging alert, traffic light sensing, etc.). More info available from www.vislab.it/proud.

Chapter 59 — Robotics in Mining

Joshua A. Marshall, Adrian Bonchis, Eduardo Nebot and Steven Scheding

This chapter presents an overview of the state of the art in mining robotics, from surface to underground applications, and beyond. Mining is the practice of extracting resources for utilitarian purposes. Today, the international business of mining is a heavily mechanized industry that exploits the use of large diesel and electric equipment. These machines must operate in harsh, dynamic, and uncertain environments such as, for example, in the high arctic, in extreme desert climates, and in deep underground tunnel networks where it can be very hot and humid. Applications of robotics in mining are broad and include robotic dozing, excavation, and haulage, robotic mapping and surveying, as well as robotic drilling and explosives handling. This chapter describes how many of these applications involve unique technical challenges for field roboticists. However, there are compelling reasons to advance the discipline of mining robotics, which include not only a desire on the part of miners to improve productivity, safety, and lower costs, but also out of a need to meet product demands by accessing orebodies situated in increasingly challenging conditions.

Autonomous haulage system

Author  Steven Scheding

Video ID : 145

This video shows the Autonomous Haulage System (AHS) implemented as part of Rio Tinto's Mine-of-the-Future initiative in North-Western Australia.

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.

Graph-based SLAM using TORO

Author  Cyrill Stachniss

Video ID : 446

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 TORO algorithm. Reference: G. Grisetti, C. Stachniss, S. Grzonka, W. Burgard. A tree parameterization for efficiently computing maximum likelihood maps using gradient descent, Proc. Robot. Sci. Syst. (RSS), Atlanta (2007)

Chapter 58 — Robotics in Hazardous Applications

James Trevelyan, William R. Hamel and Sung-Chul Kang

Robotics researchers have worked hard to realize a long-awaited vision: machines that can eliminate the need for people to work in hazardous environments. Chapter 60 is framed by the vision of disaster response: search and rescue robots carrying people from burning buildings or tunneling through collapsed rock falls to reach trapped miners. In this chapter we review tangible progress towards robots that perform routine work in places too dangerous for humans. Researchers still have many challenges ahead of them but there has been remarkable progress in some areas. Hazardous environments present special challenges for the accomplishment of desired tasks depending on the nature and magnitude of the hazards. Hazards may be present in the form of radiation, toxic contamination, falling objects or potential explosions. Technology that specialized engineering companies can develop and sell without active help from researchers marks the frontier of commercial feasibility. Just inside this border lie teleoperated robots for explosive ordnance disposal (EOD) and for underwater engineering work. Even with the typical tenfold disadvantage in manipulation performance imposed by the limits of today’s telepresence and teleoperation technology, in terms of human dexterity and speed, robots often can offer a more cost-effective solution. However, most routine applications in hazardous environments still lie far beyond the feasibility frontier. Fire fighting, remediating nuclear contamination, reactor decommissioning, tunneling, underwater engineering, underground mining and clearance of landmines and unexploded ordnance still present many unsolved problems.

IED hunters

Author  James P. Trevelyan

Video ID : 572

The video shows the work of route-clearance teams in Afghanistan.   This video has been included because researchers can see plenty of examples of realistic field conditions under which explosive-ordnance clearance is being done in Afghanistan. It is essential for researchers to have an accurate appreciation of the real field conditions before considering expensive research projects. It is also essential that researchers understand how easily insurgent forces can adapt and defeat technological solutions that have cost tens of millions of dollars to develop. Read the caption below carefully and then watch the video with this in mind. Better-quality blast-protected vehicles provide the teams with more confidence to handle challenging tasks. You will also see that improvised explosive devices (IEDs) used by insurgents are typically made from the unexploded ordnance (UXO) which the demining teams are trying to remove. Between 15% (typical failure rate for high quality US-made ammunition) and 70% (old Russian-designed ammunition) fail to explode when used.   These UXOs lie in the ground in a, at best, semi-stable state, so some easily exploded accidentally at times. Insurgents collect and attempt to disarm them, then set them up with remotely operated or vehicle-triggered detonation fuses. That is why the demining teams came to be seen as legitimate targets by insurgents, because they were removing the explosive devices the insurgency needed to fight people who they regarded as legitimate enemies. Although not explicitly acknowledged in the commentary, this video also demonstrates one of the many methods used by insurgents to adapt their techniques to defeat the highly advanced technologies available to the ISAF teams. By laying multiple devices in different locations, using different triggering devices and different deployment methods, the insurgents soon learned what the ISAF teams could and could not detect.   Every blast indicated a device that was not detected in advance by the ISAF team. Every device removed by the team indicated a device that was detected. In this way, the insurgents rapidly learned how to deploy undetectable devices that maximized their destructive power.

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 27 — Micro-/Nanorobots

Bradley J. Nelson, Lixin Dong and Fumihito Arai

The field of microrobotics covers the robotic manipulation of objects with dimensions in the millimeter to micron range as well as the design and fabrication of autonomous robotic agents that fall within this size range. Nanorobotics is defined in the same way only for dimensions smaller than a micron. With the ability to position and orient objects with micron- and nanometer-scale dimensions, manipulation at each of these scales is a promising way to enable the assembly of micro- and nanosystems, including micro- and nanorobots.

This chapter overviews the state of the art of both micro- and nanorobotics, outlines scaling effects, actuation, and sensing and fabrication at these scales, and focuses on micro- and nanorobotic manipulation systems and their application in microassembly, biotechnology, and the construction and characterization of micro and nanoelectromechanical systems (MEMS/NEMS). Material science, biotechnology, and micro- and nanoelectronics will also benefit from advances in these areas of robotics.

Linear-to-rotary motion converters for three-dimensional microscopy

Author  Lixin Dong

Video ID : 492

This video shows the application of a linear-to-rotary motion converter in 3-D imaging using a scanning electron microscope. The motion converter consists of a SiGe/Si dual-chirality helical nanobelt (DCHNB). The experiment was done using nanorobotic manipulation. Analytical and experimental investigation shows that the motion conversion has excellent linearity for small deflections. The stiffness (0.033 N/m) is much smaller than that of bottom-up synthesized helical nanostructures, which is promising for high-resolution force measurement in nanoelectromechanical systems (NEMS). The ultracompact size makes it also possible for DCHNBs to serve as rotary stages for creating 3-D scanning probe microscopes or microgoniometers.

Chapter 74 — Learning from Humans

Aude G. Billard, Sylvain Calinon and Rüdiger Dillmann

This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. We start with a brief historical overview of the field. We then summarize the various approaches taken to solve four main questions: when, what, who and when to imitate. We emphasize the importance of choosing well the interface and the channels used to convey the demonstrations, with an eye on interfaces providing force control and force feedback. We then review algorithmic approaches to model skills individually and as a compound and algorithms that combine learning from human guidance with reinforcement learning. We close with a look on the use of language to guide teaching and a list of open issues.

Learning from failure II

Author  Aude Billard

Video ID : 477

This video illustrates in a second example how learning from demonstration can benefit from failed demonstrations (as opposed to learning from successful demonstrations). Here, the robot Robota must learn how to coordinate its two arms in a timely manner for the left arm to hit the ball with the racket right on time, after the left arm sent the ball flying by hitting the catapult. More details on this work is available in: A. Rai, G. de Chambrier, A. Billard: Learning from failed demonstrations in unreliable systems, Proc. IEEE-RAS Int. Conf. Humanoid Robots (Humanoids), Atlanta (2013), pp. 410 – 416; doi: 10.1109/HUMANOIDS.2013.7030007 .

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 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.

Modsnake pole climb

Author  Howie Choset

Video ID : 171

CMU Modsnake climbing and navigating curves on a jagged pole.