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.

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.

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.

Unmanned aircraft systems (UASs) have drawn increasing attention recently, owing to advancements in related research, technology, and applications. While having been deployed successfully in military scenarios for decades, civil use cases have lately been tackled by the robotics research community.

This chapter overviews the core elements of this highly interdisciplinary field; the reader is guided through the design process of aerial robots for various applications starting with a qualitative characterization of different types of UAS. Design and modeling are closely related, forming a typically iterative process of drafting and analyzing the related properties. Therefore, we overview aerodynamics and dynamics, as well as their application to fixed-wing, rotary-wing, and flapping-wing UAS, including related analytical tools and practical guidelines. Respecting use-case-specific requirements and core autonomous robot demands, we finally provide guidelines to related system integration challenges.

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.

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.

The growth of medical robotics since the mid- 1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. This chapter will discuss some major themes and illustrate them with examples from current and past research. Further reading providing a more comprehensive review of this rapidly expanding field is suggested in Sect. 63.4.

Medical robotsmay be classified in many ways: by manipulator design (e.g., kinematics, actuation); by level of autonomy (e.g., preprogrammed versus teleoperation versus constrained cooperative control), by targeted anatomy or technique (e.g., cardiac, intravascular, percutaneous, laparoscopic, microsurgical); or intended operating environment (e.g., in-scanner, conventional operating room). In this chapter, we have chosen to focus on the role of medical robots within the context of larger computer-integrated systems including presurgical planning, intraoperative execution, and postoperative assessment and follow-up.

First, we introduce basic concepts of computerintegrated surgery, discuss critical factors affecting the eventual deployment and acceptance of medical robots, and introduce the basic system paradigms of surgical computer-assisted planning, execution, monitoring, and assessment (surgical CAD/CAM) and surgical assistance. In subsequent sections, we provide an overview of the technology ofmedical robot systems and discuss examples of our basic system paradigms, with brief additional discussion topics of remote telesurgery and robotic surgical simulators. We conclude with some thoughts on future research directions and provide suggested further reading.

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].