This chapter presents an introduction to the kinematics and dynamics of parallel mechanisms, also referred to as parallel robots. As opposed to classical serial manipulators, the kinematic architecture of parallel robots includes closed-loop kinematic chains. As a consequence, their analysis differs considerably from that of their serial counterparts. This chapter aims at presenting the fundamental formulations and techniques used in their analysis.

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.

As of 2013, almost all robots have access to computer networks that offer extensive computing, memory, and other resources that can dramatically improve performance. The underlying enabling framework is the focus of this chapter: networked robots. Networked robots trace their origin to telerobots or remotely controlled robots. Telerobots are widely used to explore undersea terrains and outer space, to defuse bombs and to clean up hazardous waste. Until 1994, telerobots were accessible only to trained and trusted experts through dedicated communication channels. This chapter will describe relevant network technology, the history of networked robots as it evolves from teleoperation to cloud robotics, properties of networked robots, how to build a networked robot, example systems. Later in the chapter, we focus on the recent progress on cloud robotics, and topics for future research.

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.

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.

Educational robotics programs have become popular in most developed countries and are becoming more and more prevalent in the developing world as well. Robotics is used to teach problem solving, programming, design, physics, math and even music and art to students at all levels of their education. This chapter provides an overview of some of the major robotics programs along with the robot platforms and the programming environments commonly used. Like robot systems used in research, there is a constant development and upgrade of hardware and software – so this chapter provides a snapshot of the technologies being used at this time. The chapter concludes with a review of the assessment strategies that can be used to determine if a particular robotics program is benefitting students in the intended ways.

This chaptermay be seen as a follow up to Chap. 24, devoted to the classification and modeling of basic wheeled mobile robot (WMR) structures, and a natural complement to Chap. 47, which surveys motion planning methods for WMRs. A typical output of these methods is a feasible (or admissible) reference state trajectory for a given mobile robot, and a question which then arises is how to make the physical mobile robot track this reference trajectory via the control of the actuators with which the vehicle is equipped. The object of the present chapter is to bring elements of the answer to this question based on simple and effective control strategies.

The chapter is organized as follows. Section 49.2 is devoted to the choice of controlmodels and the determination of modeling equations associated with the path-following control problem. In Sect. 49.3, the path following and trajectory stabilization problems are addressed in the simplest case when no requirement is made on the robot orientation (i. e., position control). In Sect. 49.4 the same problems are revisited for the control of both position and orientation. The previously mentionned sections consider an ideal robot satisfying the rolling-without-sliding assumption. In Sect. 49.5, we relax this assumption in order to take into account nonideal wheel-ground contact. This is especially important for field-robotics applications and the proposed results are validated through full scale experiments on natural terrain. Finally, a few complementary issues on the feedback control of mobile robots are briefly discussed in the concluding Sect. 49.6, with a list of commented references for further reading on WMRs motion control.

Within the context of multiple mobile, and networked robot systems, this chapter explores the current state of the art. After a brief introduction, we first examine architectures for multirobot cooperation, exploring the alternative approaches that have been developed. Next, we explore communications issues and their impact on multirobot teams in Sect. 53.3, followed by a discussion of networked mobile robots in Sect. 53.4. Following this we discuss swarm robot systems in Sect. 53.5 and modular robot systems in Sect. 53.6. While swarm and modular systems typically assume large numbers of homogeneous robots, other types of multirobot systems include heterogeneous robots. We therefore next discuss heterogeneity in cooperative robot teams in Sect. 53.7. Once robot teams allow for individual heterogeneity, issues of task allocation become important; Sect. 53.8 therefore discusses common approaches to task allocation. Section 53.9 discusses the challenges of multirobot learning, and some representative approaches. We outline some of the typical application domains which serve as test beds for multirobot systems research in Sect. 53.10. Finally, we conclude in Sect. 53.11 with some summary remarks and suggestions for further reading.