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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.

High-speed magnetic microrobot actuation in a microfluidic chip by a fine V-groove surface

Author  Fumihito Arai

Video ID : 491

This video shows high-speed microrobotic actuation driven by permanent magnets in a microfluidic chip. The microrobot has a milliNewton-level output force from a permanent magnet, micrometer-level positioning accuracy, and drive speed of over 280 mm/s. The riblet surface, which is a regularly arrayed V-groove, reduces fluid friction and enables high-speed actuation. Ni- and Si-composite fabrication was employed to form the optimum riblet shape on the microrobot’s surface by wet and dry etching. The evaluation experiments show that the microrobot can be actuated at a rate of up to 90 Hz, which is more than ten times higher than that of the microrobot without a riblet.

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.

Treebot: Autonomous tree climbing by tactile sensing

Author  Tin Lun Lam, Yangsheng Xu

Video ID : 289

The design of Treebot is unique: It uses a set of flexible linear actuators connecting two gripping claws to enable it to move around like an inchworm. While the back gripper holds on, the front gripper releases and the body extends forward, enabling the robot to literally feel around for a good place to grip.

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

Author  Monica Nicolescu

Video ID : 28

This is a video recorded in early 2000s, showing a Pioneer robot visiting a number of targets in a certain order based on a demonstration provided by a human user. 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 9 — Force Control

Luigi Villani and Joris De Schutter

A fundamental requirement for the success of a manipulation task is the capability to handle the physical contact between a robot and the environment. Pure motion control turns out to be inadequate because the unavoidable modeling errors and uncertainties may cause a rise of the contact force, ultimately leading to an unstable behavior during the interaction, especially in the presence of rigid environments. Force feedback and force control becomes mandatory to achieve a robust and versatile behavior of a robotic system in poorly structured environments as well as safe and dependable operation in the presence of humans. This chapter starts from the analysis of indirect force control strategies, conceived to keep the contact forces limited by ensuring a suitable compliant behavior to the end effector, without requiring an accurate model of the environment. Then the problem of interaction tasks modeling is analyzed, considering both the case of a rigid environment and the case of a compliant environment. For the specification of an interaction task, natural constraints set by the task geometry and artificial constraints set by the control strategy are established, with respect to suitable task frames. This formulation is the essential premise to the synthesis of hybrid force/motion control schemes.

COMRADE: Compliant motion research and development environment

Author  Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel et al.

Video ID : 691

The video collects works on force control developed in the 1970s-1980s and 1990s at the Department of Mechanical Engineering of the Katholieke Universiteit Leuven, Belgium. The tasks were programmed and simulated using the task-frame-based software package COMRADE (compliant motion research and development environment). The video was recorded in the mid-1990s. The main references for the video are: 1. H. Van Brussel, J. Simons: The adaptable compliance concept and its use for automatic assembly by active force feedback accommodations, Proc. 9th Int. Symposium Indust. Robot., Washington (1979), pp.167-181 2. J. Simons, H. Van Brussel, J. De Schutter, J. Verhaert: A self-learning automaton with variable resolution for high precision assembly by industrial robots, IEEE Trans. Autom. Control 27(5), 1109-1113 (1982) 3. J. De Schutter, H. Van Brussel: Compliant robot motion II. A control approach based on external control loops, Int. J. Robot. Res. 7(4), 18-33 (1988) 3.J. De Schutter, H. Van Brussel: Compliant robot motion I. A formalism for specifying compliant motion tasks, Int. J. Robot. Res. 7(4), 3-17 (1988) 4. W. Witvrouw, P. Van de Poel, H. Bruyninckx, J. De Schutter: ROSI: A task specification and simulation tool for force-sensor-based robot control, Proc. 24th Int. Symp. Indust. Robot., Tokyo (1993), pp. 385-392 5. W. Witvrouw, P. Van de Poel, J. De Schutter: COMRADE: Compliant motion research and development environment, Proc. 3rd IFAC/IFIP Workshop on Algorithms and Architecture for Real-Time Control. Ostend (1995), pp. 81-87 6. H. Bruyninckx, S. Dutre, J. De Schutter: Peg-on-hole, a model-based solution to peg and hole alignment, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Nagoya (1995), pp. 1919-1924 7. M. Nuttin, H. Van Brussel: Learning the peg-into-hole assembly operation with a connectionist reinforcement technique, Comput. Ind. 33(1), 101-109 (1997)

Chapter 45 — World Modeling

Wolfram Burgard, Martial Hebert and Maren Bennewitz

In this chapter we describe popular ways to represent the environment of a mobile robot. For indoor environments, which are often stored using two-dimensional representations, we discuss occupancy grids, line maps, topologicalmaps, and landmark-based representations. Each of these techniques has its own advantages and disadvantages. Whilst occupancy grid maps allow for quick access and can efficiently be updated, line maps are more compact. Also landmark-basedmaps can efficiently be updated and maintained, however, they do not readily support navigation tasks such as path planning like topological representations do.

Additionally, we discuss approaches suited for outdoor terrain modeling. In outdoor environments, the flat-surface assumption underling many mapping techniques for indoor environments is no longer valid. A very popular approach in this context are elevation and variants maps, which store the surface of the terrain over a regularly spaced grid. Alternatives to such maps are point clouds, meshes, or three-dimensional grids, which provide a greater flexibility but have higher storage demands.

Service-robot navigation in urban environments

Author  Christian Siagian

Video ID : 270

This video presents the navigation system of the Beobot service robot of the iLab, University of Southern California (USC). Beobot's task is to fulfill services in urban-like environments, especially those involving long-range travel. The robot uses a topological map for global localization based on acquired images.

Chapter 67 — Humanoids

Paul Fitzpatrick, Kensuke Harada, Charles C. Kemp, Yoshio Matsumoto, Kazuhito Yokoi and Eiichi Yoshida

Humanoid robots selectively immitate aspects of human form and behavior. Humanoids come in a variety of shapes and sizes, from complete human-size legged robots to isolated robotic heads with human-like sensing and expression. This chapter highlights significant humanoid platforms and achievements, and discusses some of the underlying goals behind this area of robotics. Humanoids tend to require the integration ofmany of the methods covered in detail within other chapters of this handbook, so this chapter focuses on distinctive aspects of humanoid robotics with liberal cross-referencing.

This chapter examines what motivates researchers to pursue humanoid robotics, and provides a taste of the evolution of this field over time. It summarizes work on legged humanoid locomotion, whole-body activities, and approaches to human–robot communication. It concludes with a brief discussion of factors that may influence the future of humanoid robots.

Footstep planning modeled as a whole-body, inverse-kinematic problem

Author  Eiichi Yoshida

Video ID : 596

An augmented-robot structure was introduced as "virtual" planar links attached to a foot that represents footsteps. This modeling makes it possible to solve the footstep planning as a problem of inverse kinematics, and also to determine the final whole-body configuration. After planning the footsteps, the dynamically-stable, whole-body motion including walking can be computed by using a dynamic pattern generator.

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 localization of a power drill

Author  Kaijen Hsiao

Video ID : 77

This video shows a Barrett WAM arm tactilely localizing and reorienting a power drill under high positional uncertainty. The goal is for the robot to robustly grasp the power drill such that the trigger can be activated. The robot tracks the distribution of possible object poses on the table over a 3-D grid (the belief space). It then selects between information-gathering, reorienting, and goal-seeking actions by modeling the problem as a POMDP (partially observable Markov decision process) and using receding-horizon, forward search through the belief space. In the video, the inset window with the simulated robot is a visualization of the current belief state. The red spheres sit at the vertices of the object mesh placed at the most likely state, and the dark-blue box also shows the location of the most likely state. The purple box shows the location of the mean of the belief state, and the light-blue boxes show the variance of the belief state in the form of the locations of various states that are one standard deviation away from the mean in each of the three dimensions of uncertainty (x, y, and theta). The magenta spheres and arrows that appear when the robot touches the object show the contact locations and normals as reported by the sensors, and the cyan spheres that largely overlap the hand show where the robot controllers are trying to move the hand.

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.

Toto

Author  Maja J. Mataric

Video ID : 35

This is a video of the work done early 1990, showing Toto which introduced the use of distributed representation into behavior-based systems. Reference: M.J. Matarić: Integration of representation into goal-driven behavior-based robots, IEEE Trans. Robot. Autom. 8(3), 304–312 (1992)

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.

Teleoperated humanoid robot - HRP: Tele-driving of lifting vehicle

Author  Masami Kobayashi, Hisashi Moriyama, Toshiyuki Itoko, Yoshitaka Yanagihara, Takao Ueno, Kazuhisa Ohya, Kazuhito Yokoi

Video ID : 319

This video shows the teleoperation a humanoid robot HRP using whole-body multimodal tele-existence system. The human operator teleoperates the humanoid robot to drive a lifting vehicle in a warehouse. Presented at ICRA 2002.

Chapter 56 — Robotics in Agriculture and Forestry

Marcel Bergerman, John Billingsley, John Reid and Eldert van Henten

Robotics for agriculture and forestry (A&F) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.

The Intelligent Autonomous Weeder

Author  Tijmen Bakker, Kees Van Asselt, Jan Bontsema, Joachim Müller, Gerrit van Straten

Video ID : 310

The Intelligent Autonomous Weeder is a four-wheel, steered, four-wheel-drive, autonomous platform which can be used for autonomous weeding operations in arable farming. An RTK DGPS system is used for navigation. The control architecture is based on a hybrid deliberative and behavior-based reactive approach.