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Chapter 61 — Robot Surveillance and Security

Wendell H. Chun and Nikolaos Papanikolopoulos

This chapter introduces the foundation for surveillance and security robots for multiple military and civilian applications. The key environmental domains are mobile robots for ground, aerial, surface water, and underwater applications. Surveillance literallymeans to watch fromabove,while surveillance robots are used to monitor the behavior, activities, and other changing information that are gathered for the general purpose of managing, directing, or protecting one’s assets or position. In a practical sense, the term surveillance is taken to mean the act of observation from a distance, and security robots are commonly used to protect and safeguard a location, some valuable assets, or personal against danger, damage, loss, and crime. Surveillance is a proactive operation,while security robots are a defensive operation. The construction of each type of robot is similar in nature with amobility component, sensor payload, communication system, and an operator control station.

After introducing the major robot components, this chapter focuses on the various applications. More specifically, Sect. 61.3 discusses the enabling technologies of mobile robot navigation, various payload sensors used for surveillance or security applications, target detection and tracking algorithms, and the operator’s robot control console for human–machine interface (HMI). Section 61.4 presents selected research activities relevant to surveillance and security, including automatic data processing of the payload sensors, automaticmonitoring of human activities, facial recognition, and collaborative automatic target recognition (ATR). Finally, Sect. 61.5 discusses future directions in robot surveillance and security, giving some conclusions and followed by references.

Surveillance by a drone

Author  Bernd Lutz

Video ID : 554

The MULTIROTOR by is an innovative measuring instrument that can be used for surveillance. Besides delivering very stable pictures, the MULTIROTOR is also able to fly fully-automated measurement flights with a high precision of 1 mm ground resolution and equally impressive flight stability at wind strengths up to 10-15 m/s.

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].

MIT Compass Gait Robot - Locomotion over rough terrain

Author  Fumiya Iida, Auke Ijspeert

Video ID : 111

This video shows an experiment of the MIT Compass Gait Robot for locomotion over rough terrain. This platform takes advantage of point-feet of compass-gait robots which are usually advantageous for locomotion in challenging, rough terrains. The motion controller uses a simple oscillator because of the intrinsic dynamic stability of this robot.

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.

IBVS on a 6-DOF robot arm (2)

Author  Francois Chaumette, Seth Hutchinson, Peter Corke

Video ID : 60

This video shows an IBVS on a 6-DOF robot arm with Cartesian coordinates of image points as visual features and a current interaction matrix in the control scheme. It corresponds to the results depicted in Figure 34.3.

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.

Salamandra robotica II robot walking and swimming

Author  Alessandro Crespi, Konstantinos Karakasiliotis, Andre Guignard, Auke Jan Ijspeert

Video ID : 395

Salamandra robotica II walking and swimming outdoors and performing the transition from swimming to walking indoors. The transition between two different locomotions and the locomotions themselves is generated by central pattern generation (CPG) and simulation of a mesencephalic locomotor region (MLR). Video from the Ecole Polytechnique Federale de Lausanne Biorobotics Lab.

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.

Neptus command and control infrastructure

Author  Laboratario de Sistemas e Tecnologias Subaquaticas - Porto University

Video ID : 324

See how Neptus is used to plan, simulate, monitor and review missions performed by autonomous vehicles. Neptus, originally developed at the Underwater Systems and Technology Laboratory, is open source software available from / NOPTILUS project [NOPTILUS is funded by European Community's Seventh Framework Programme ICT-FP]

Chapter 71 — Cognitive Human-Robot Interaction

Bilge Mutlu, Nicholas Roy and Selma Šabanović

A key research challenge in robotics is to design robotic systems with the cognitive capabilities necessary to support human–robot interaction. These systems will need to have appropriate representations of the world; the task at hand; the capabilities, expectations, and actions of their human counterparts; and how their own actions might affect the world, their task, and their human partners. Cognitive human–robot interaction is a research area that considers human(s), robot(s), and their joint actions as a cognitive system and seeks to create models, algorithms, and design guidelines to enable the design of such systems. Core research activities in this area include the development of representations and actions that allow robots to participate in joint activities with people; a deeper understanding of human expectations and cognitive responses to robot actions; and, models of joint activity for human–robot interaction. This chapter surveys these research activities by drawing on research questions and advances from a wide range of fields including computer science, cognitive science, linguistics, and robotics.

Robotic secrets revealed, Episode 2: The trouble begins

Author  Greg Trafton

Video ID : 130

This video demonstrates research on robot perception (including object recognition and multimodal person identification) and embodied cognition (including theory of mind or the ability to reason about what others believe). The video features two people interacting with two robots.

Chapter 70 — Human-Robot Augmentation

Massimo Bergamasco and Hugh Herr

The development of robotic systems capable of sharing with humans the load of heavy tasks has been one of the primary objectives in robotics research. At present, in order to fulfil such an objective, a strong interest in the robotics community is collected by the so-called wearable robots, a class of robotics systems that are worn and directly controlled by the human operator. Wearable robots, together with powered orthoses that exploit robotic components and control strategies, can represent an immediate resource also for allowing humans to restore manipulation and/or walking functionalities.

The present chapter deals with wearable robotics systems capable of providing different levels of functional and/or operational augmentation to the human beings for specific functions or tasks. Prostheses, powered orthoses, and exoskeletons are described for upper limb, lower limb, and whole body structures. State-of-theart devices together with their functionalities and main components are presented for each class of wearable system. Critical design issues and open research aspects are reported.


Author  Massimo Bergamasco

Video ID : 148

The video details the Arm-Exos and, in particular, its capability for tracking the operator's motions and for rendering the contact forces in a simple, demonstrative, virtual environment.

Chapter 78 — Perceptual Robotics

Heinrich Bülthoff, Christian Wallraven and Martin A. Giese

Robots that share their environment with humans need to be able to recognize and manipulate objects and users, perform complex navigation tasks, and interpret and react to human emotional and communicative gestures. In all of these perceptual capabilities, the human brain, however, is still far ahead of robotic systems. Hence, taking clues from the way the human brain solves such complex perceptual tasks will help to design better robots. Similarly, once a robot interacts with humans, its behaviors and reactions will be judged by humans – movements of the robot, for example, should be fluid and graceful, and it should not evoke an eerie feeling when interacting with a user. In this chapter, we present Perceptual Robotics as the field of robotics that takes inspiration from perception research and neuroscience to, first, build better perceptual capabilities into robotic systems and, second, to validate the perceptual impact of robotic systems on the user.

Active in-hand object recognition

Author  Christian Wallraven

Video ID : 569

This video showcases the implementation of active object learning and recognition using the framework proposed in Browatzki et al. [1, 2]. The first phase shows the robot trying to learn the visual representation of several paper cups differing by a few key features. The robot executes a pre-programmed exploration program to look at the cup from all sides. The (very low-resolution) visual input is tracked and so-called key-frames are extracted which represent the (visual) exploration. After learning, the robot tries to recognize cups that have been placed into its hands using a similar exploration program based on visual information - due to the low-resolution input and the highly similar objects, the robot, however, fails to make the correct decision. The video then shows the second, advanced, exploration, which is based on actively seeking the view that is expected to provide maximum information about the object. For this, the robot embeds the learned visual information into a proprioceptive map indexed by the two joint angles of the hand. In this map, the robot now tries to predict the joint-angle combination that provides the most information about the object, given the current state of exploration. The implementation uses particle filtering to track a large number of object (view) hypotheses at the same time. Since the robot now uses a multisensory representation, the subsequent object-recognition trials are all correct, despite poor visual input and highly similar objects. References: [1] B Browatzki, V. Tikhanoff, G. Metta, H.H. Bülthoff, C. Wallraven: Active in-hand object recognition on a humanoid robot, IEEE Trans. Robot. 30(5), 1260-1269 (2014); [2] B. Browatzki, V. Tikhanoff, G. Metta, H.H. Bülthoff, C. Wallraven: Active object recognition on a humanoid robot, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), St. Paul (2012), pp. 2021-2028.

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.

Full body compliant humanoid COMAN

Author  IIT - Advanced Robotics

Video ID : 698

The compliant humanoid COMAN is developed by the Department of Advanced Robotics (ADVR), Istituto Italiano di Tecnologia (IIT). All the achievements shown in this video are attributed to the team work of the Humanoid Group in ADVR, IIT.

Chapter 68 — Human Motion Reconstruction

Katsu Yamane and Wataru Takano

This chapter presents a set of techniques for reconstructing and understanding human motions measured using current motion capture technologies. We first review modeling and computation techniques for obtaining motion and force information from human motion data (Sect. 68.2). Here we show that kinematics and dynamics algorithms for articulated rigid bodies can be applied to human motion data processing, with help from models based on knowledge in anatomy and physiology. We then describe methods for analyzing human motions so that robots can segment and categorize different behaviors and use them as the basis for human motion understanding and communication (Sect. 68.3). These methods are based on statistical techniques widely used in linguistics. The two fields share the common goal of converting continuous and noisy signal to discrete symbols, and therefore it is natural to apply similar techniques. Finally, we introduce some application examples of human motion and models ranging from simulated human control to humanoid robot motion synthesis.

Converting human motion to sentences

Author  Katsu Yamane

Video ID : 766

This video shows an example of converting human motion sequences to descriptive sentences.