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Chapter 76 — Evolutionary Robotics

Stefano Nolfi, Josh Bongard, Phil Husbands and Dario Floreano

Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.

Evolution of collision-free navigation

Author  Dario Floreano

Video ID : 39

In their initial generations, robots can hardly avoid walls (one robot even approaches objects). After 50 generations, robots can navigate around the looping maze without hitting the walls.

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.

Sparse pose adjustment

Author  Kurt Konolige

Video ID : 447

This video shows an illustration of pose-graph SLAM optimization, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using sparse pose adjustment. Reference: K. Konolige, G. Grisetti, R. Kümmerle, W. Burgard, B. Limketkai, R. Vincent: Sparse pose adjustment for 2-D mapping, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Taipei (2010).

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.

Speed-sign detection

Author  Alberto Broggi, Alexander Zelinsky, Ümit Ozgüner, Christian Laugier

Video ID : 838

This video demonstrates robust speed-sign extraction under variable conditions, i.e., day and night. The system uses the unique radial symmetry detector to achieve robustness.

Chapter 64 — Rehabilitation and Health Care Robotics

H.F. Machiel Van der Loos, David J. Reinkensmeyer and Eugenio Guglielmelli

The field of rehabilitation robotics considers robotic systems that 1) provide therapy for persons seeking to recover their physical, social, communication, or cognitive function, and/or that 2) assist persons who have a chronic disability to accomplish activities of daily living. This chapter will discuss these two main domains and provide descriptions of the major achievements of the field over its short history and chart out the challenges to come. Specifically, after providing background information on demographics (Sect. 64.1.2) and history (Sect. 64.1.3) of the field, Sect. 64.2 describes physical therapy and exercise training robots, and Sect. 64.3 describes robotic aids for people with disabilities. Section 64.4 then presents recent advances in smart prostheses and orthoses that are related to rehabilitation robotics. Finally, Sect. 64.5 provides an overview of recent work in diagnosis and monitoring for rehabilitation as well as other health-care issues. The reader is referred to Chap. 73 for cognitive rehabilitation robotics and to Chap. 65 for robotic smart home technologies, which are often considered assistive technologies for persons with disabilities. At the conclusion of the present chapter, the reader will be familiar with the history of rehabilitation robotics and its primary accomplishments, and will understand the challenges the field may face in the future as it seeks to improve health care and the well being of persons with disabilities.

Kineassist

Author  Discover Channel/Michael Peshkin

Video ID : 505

The Kineassist is a gait-training robot which rolls behind a patient and compliantly supports the trunk and pelvis. It enables patients to challenge the limits of their stability, catching them if they fall.

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.

The FLEA: Flea-inspired, light jumping robot using elastic catapult with active storage and release mechanism

Author  Minkyun Noh, Seung-Won Kim, Sungmin An, Je-Sung Koh, Kyu-Jin Cho

Video ID : 281

The FLEA: flea-inspired, light jumping robot using elastic catapult with active storage and release mechanism. The robot was created to realize a flea-inspired catapult mechanism with shape-memory-alloy (SMA) spring actuators and a smart composite microstructure. The robot was fabricated with a weight of 1.1 g and a 2 cm body size, so that it can jump a distance of up to 30 times its body size.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

Human-robot interactions

Author   J.Y.S. Luh, Shuyi Hu

Video ID : 613

In human-robot cooperative tasks, the robot is required to memorize different trajectories for different assignments and to automatically retrieve a proper one from them in real-time for the robot to follow when any assignment is repeated as, e.g., when carrying a rigid object jointly by a human and a robot. To start the task, the human leads the robot along a suitable trajectory and thereby achieves the desired goal. For every new task, the human is required to lead the robot. During the process, the trajectories are recorded and stored in memory as "skillful trajectories" for later use. Reference: J.Y.S. Luh, S. Hu: Interactions and motions in human-robot coordination, Proc. IEEE Int. Robot. Autom. (ICRA), Detroit (1999), Vol. 4, pp. 3171 – 3176; doi: 10.1109/ROBOT.1999.774081.

Chapter 32 — 3-D Vision for Navigation and Grasping

Danica Kragic and Kostas Daniilidis

In this chapter, we describe algorithms for three-dimensional (3-D) vision that help robots accomplish navigation and grasping. To model cameras, we start with the basics of perspective projection and distortion due to lenses. This projection from a 3-D world to a two-dimensional (2-D) image can be inverted only by using information from the world or multiple 2-D views. If we know the 3-D model of an object or the location of 3-D landmarks, we can solve the pose estimation problem from one view. When two views are available, we can compute the 3-D motion and triangulate to reconstruct the world up to a scale factor. When multiple views are given either as sparse viewpoints or a continuous incoming video, then the robot path can be computer and point tracks can yield a sparse 3-D representation of the world. In order to grasp objects, we can estimate 3-D pose of the end effector or 3-D coordinates of the graspable points on the object.

LIBVISO: Visual odometry for intelligent vehicles

Author  Andreas Geiger

Video ID : 122

This video demonstrates a visual-odometry algorithm on the performance of the vehicle Annieway (VW Passat). Visual odometry is the estimation of a video camera's 3-D motion and orientation, which is purely based on stereo vision in this case. The blue trajectory is the motion estimated by visual odometry, and the red trajectory is the ground truth by a high-precision OXTS RT3000 GPS+IMU system. The software is available from http://www.cvlibs.net/

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.

Adaptive L1 depth control of a ROV

Author  Divine Maalouf, Vincent Creuze, Ahmed Chemori

Video ID : 267

This video illustrates the ability of the L1 adaptive controller to deal with parameter changes (buoyancy) and to reject disturbances (impacts, tether movements, etc.). This controller is implemented on a modified version of the AC-ROV underwater vehicle to perform depth regulation. This work was conducted at LIRMM (University Montpellier 2 / CNRS) in collaboration with Tecnalia France.

Chapter 10 — Redundant Robots

Stefano Chiaverini, Giuseppe Oriolo and Anthony A. Maciejewski

This chapter focuses on redundancy resolution schemes, i. e., the techniques for exploiting the redundant degrees of freedom in the solution of the inverse kinematics problem. This is obviously an issue of major relevance for motion planning and control purposes.

In particular, task-oriented kinematics and the basic methods for its inversion at the velocity (first-order differential) level are first recalled, with a discussion of the main techniques for handling kinematic singularities. Next, different firstorder methods to solve kinematic redundancy are arranged in two main categories, namely those based on the optimization of suitable performance criteria and those relying on the augmentation of the task space. Redundancy resolution methods at the acceleration (second-order differential) level are then considered in order to take into account dynamics issues, e.g., torque minimization. Conditions under which a cyclic task motion results in a cyclic joint motion are also discussed; this is a major issue when a redundant manipulator is used to execute a repetitive task, e.g., in industrial applications. The use of kinematic redundancy for fault tolerance is analyzed in detail. Suggestions for further reading are given in a final section.

Configuration space control of KUKA Lightweight Robot LWR with EXARM Exoskeleton

Author  Telerobotics Lab

Video ID : 817

This video shows some advanced inverse kinematics mapping that enables the control of a redundant manipulator (KUKA LWR) by means of Cartesian location and geometric correspondence to the human arm. Thereby the null-space of the robot manipulator can be exploited to enable very intuitive operations. Joint limits and singularities are avoided, as well, by optimized mounting of the robot and the hand.

Chapter 69 — Physical Human-Robot Interaction

Sami Haddadin and Elizabeth Croft

Over the last two decades, the foundations for physical human–robot interaction (pHRI) have evolved from successful developments in mechatronics, control, and planning, leading toward safer lightweight robot designs and interaction control schemes that advance beyond the current capacities of existing high-payload and highprecision position-controlled industrial robots. Based on their ability to sense physical interaction, render compliant behavior along the robot structure, plan motions that respect human preferences, and generate interaction plans for collaboration and coaction with humans, these novel robots have opened up novel and unforeseen application domains, and have advanced the field of human safety in robotics.

This chapter gives an overview on the state of the art in pHRI as of the date of publication. First, the advances in human safety are outlined, addressing topics in human injury analysis in robotics and safety standards for pHRI. Then, the foundations of human-friendly robot design, including the development of lightweight and intrinsically flexible force/torque-controlled machines together with the required perception abilities for interaction are introduced. Subsequently, motionplanning techniques for human environments, including the domains of biomechanically safe, risk-metric-based, human-aware planning are covered. Finally, the rather recent problem of interaction planning is summarized, including the issues of collaborative action planning, the definition of the interaction planning problem, and an introduction to robot reflexes and reactive control architecture for pHRI.

A control strategy for human-friendly robots

Author   Jochen Heinzmann, Jon Kieffer, Alexander Zelinsky

Video ID : 611

The video shows the basic behavior of the system: A zero-gravity simulation with a Barrett Whole Arm Manipulator (WAM), a lightweight, 7-DOF robot driven by cable drives. The zero-G module applies the appropriate motor torques to counteract the gravity effects. The psychological impression is that the robot is completely passive although considerable forces are required for the gravity compensation. The robot is slowed down by friction only. In the second part of the video, the gravity constant is increased by 60%. This causes the robot to float up into a vertical configuration, as if the robot would be mounted upside down. (Video Proceedings of the Int. Conf. Robot. Autom. (ICRA), 1999)