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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.

A single-motor-actuated, miniature, steerable jumping robot

Author  Jianguo Zhao, Jing Xu, Bingtuan Gao, Ning Xi, Fernando J. Cintron, Matt W. Mutka, Li Xiao

Video ID : 280

The contents of the video are divided into three parts. The first part illustrates the individual functions of the robot such as jumping, self-righting and steering. The second part demonstrates the robot's locomotion capability in indoor environments. Scenarios such as jumping from the floor, jumping in an office and jumping over stairs are included. The third part shows the robot's locomotion capability in outdoor environments. Experiments on uneven ground, ground with small gravels and ground with grass are included.

Chapter 36 — Motion for Manipulation Tasks

James Kuffner and Jing Xiao

This chapter serves as an introduction to Part D by giving an overview of motion generation and control strategies in the context of robotic manipulation tasks. Automatic control ranging from the abstract, high-level task specification down to fine-grained feedback at the task interface are considered. Some of the important issues include modeling of the interfaces between the robot and the environment at the different time scales of motion and incorporating sensing and feedback. Manipulation planning is introduced as an extension to the basic motion planning problem, which can be modeled as a hybrid system of continuous configuration spaces arising from the act of grasping and moving parts in the environment. The important example of assembly motion is discussed through the analysis of contact states and compliant motion control. Finally, methods aimed at integrating global planning with state feedback control are summarized.

A square peg-in-hole demonstration using manipulation skills

Author  Unknown

Video ID : 362

This video shows a square peg-in-hole demonstration using manipulation skills which refer to a set of motion primitives derived from the analysis of assembly tasks. This video demonstrated three manipulation skills: move-to-touch skill, rotate-to-level skill, and rotate-to-insert skill, which are executed to insert a square peg into a hole.

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.

Side-looking multipulse sonar moving down cinder-block hallway

Author  Roman Kuc

Video ID : 303

Rather than producing a single TOF reading per emission, the multipulse sonar produces multiple spikes by quickly resetting the sonar-detector integrator, thereby producing a spike density related to the echo amplitude. A side-looking sonar scans a cinder-block wall containing a door and window jambs. The resulting spikes have been processed to differentiate the first cinder-block wall, the cider-block surface and localize the window and door jambs. The red circles indicate the initial TOF values and illustrate the additional echo waveform data produced by the multipulse sonar. Reference: R. Kuc: Recognizing retro-reflectors with an obliquely-oriented multi-point sonar and acoustic flow, Int. J. Robot. Res. 22(2), 129-145, (2003); doi:10.1177/0278364903022002004.

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.

Catching objects in flight

Author  Seungsu Kim, Ashwini Shukla, Aude Billard

Video ID : 653

We target the difficult problem of catching in-flight objects with uneven shapes. This requires the solution of three complex problems: predicting accurately the trajectory of fast-moving objects, predicting the feasible catching configuration, and planning the arm motion, all within milliseconds. We follow a programming-by-demonstration approach in order to learn models of the object and the arm dynamics from throwing examples. We propose a new methodology for finding a feasible catching configuration in a probabilistic manner. We leverage the strength of dynamical systems for encoding motion from several demonstrations. This enables fast and online adaptation of the arm motion in the presence of sensor uncertainty. We validate the approach in simulation with the iCub humanoid robot and in real-world experiment with the KUKA LWR 4+ (7-DOF arm robot) for catching a hammer, a tennis racket, an empty bottle, a partially filled bottle and a cardboard box.

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.

Collaborative control of the Body Extender

Author  Massimo Bergamasco

Video ID : 151

The video shows the numerical and experimental validation of the collaborative control applied to the Body Extender. The control prevents the overturning of the system under the action of gravity by minimally distorting the operator's intended motion.

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.

Tracking people for security

Author  Nikos Papanikolopoulos

Video ID : 683

Tracking of people in crowded scenes is challenging because people occlude each other as they walk around. The latest revision of the University of Minnesota's person tracker uses adaptive appearance models that explicitly account for the probability that a person may be partially occluded. All potentially occluding targets are tracked jointly, and the most likely visibility order is estimated (so we know the probability that person A is occluding person B). Target-size adaptation is performed using calibration information about the camera, and the reported target positions are made in real-world coordinates.

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.

PI piezo hexapod

Author  Physik Instrumente

Video ID : 648

Fig. 4.26 A six-axis Physik Instrumente (PI) piezo hexapod with sub-nanometer resolution.

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 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.

OctArms I-V

Author  Ian Walker

Video ID : 158

Video showing five different iterations of the OctArm continuum manipulator.

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.

Control experiments for a flexible-joint robot arm

Author  Mark Spong

Video ID : 135

This 1989 video is about an experimental single-link arm with an elastic joint moving under gravity, which was developed at the Coordinated Science Lab of the University of Illinois at Urbana. The video illustrates some of the control techniques that are mentioned in the first part of Chap. 11: e.g., computed torque using the rigid model (unstable), feedback linearizing control including elasticity (perfect design), and corrective control based on singular perturbation analysis (and its adaptive version). Reference: F. Ghorbel, J.Y. Hung, M.W. Spong: Adaptive control of flexible joint robots, IEEE Control Syst. Mag. 9(7), 9-13 (1989) doi: 10.1109/37.41450