Robotic manipulators use contact forces to grasp and manipulate objects in their environments. Fixtures rely on contacts to immobilize workpieces. Mobile robots and humanoids use wheels or feet to generate the contact forces that allow them to locomote. Modeling of the contact interface, therefore, is fundamental to analysis, design, planning, and control of many robotic tasks.

This chapter presents an overview of the modeling of contact interfaces, with a particular focus on their use in manipulation tasks, including graspless or nonprehensile manipulation modes such as pushing. Analysis and design of grasps and fixtures also depends on contact modeling, and these are discussed in more detail in Chap. 38. Sections 37.2–37.5 focus on rigid-body models of contact. Section 37.2 describes the kinematic constraints caused by contact, and Sect. 37.3 describes the contact forces that may arise with Coulomb friction. Section 37.4 provides examples of analysis of multicontact manipulation tasks with rigid bodies and Coulomb friction. Section 37.5 extends the analysis to manipulation by pushing. Section 37.6 introduces modeling of contact interfaces, kinematic duality, and pressure distribution and soft contact interface. Section 37.7 describes the concept of the friction limit surface and illustrates it with an example demonstrating the construction of a limit surface for a soft contact. Finally, Sect. 37.8 discusses how these more accurate models can be used in fixture analysis and design.

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 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.

Much of the technology that makes robots reliable, human friendly, and adaptable for numerous applications has emerged from manufacturers of industrial robots. With an estimated installation base in 2014 of about 1:5million units, some 171 000 new installations in that year and an annual turnover of the robotics industry estimated to be US$ 32 billion, industrial robots are by far the largest commercial application of robotics technology today.

The foundations for robot motion planning and control were initially developed with industrial applications in mind. These applications deserve special attention in order to understand the origin of robotics science and to appreciate the many unsolved problems that still prevent the wider use of robots in today’s agile manufacturing environments. In this chapter, we present a brief history and descriptions of typical industrial robotics applications and at the same time we address current critical state-of-the-art technological developments. We show how robots with differentmechanisms fit different applications and how applications are further enabled by latest technologies, often adopted from technological fields outside manufacturing automation.

We will first present a brief historical introduction to industrial robotics with a selection of contemporary application examples which at the same time refer to a critical key technology. Then, the basic principles that are used in industrial robotics and a review of programming methods will be presented. We will also introduce the topic of system integration particularly from a data integration point of view. The chapter will be closed with an outlook based on a presentation of some unsolved problems that currently inhibit wider use of industrial robots.

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.

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.

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.