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 key research challenge in robotics is to design robotic systems with the cognitive capabilities necessary to support human–robot interaction. These systems will need to have appropriate representations of the world; the task at hand; the capabilities, expectations, and actions of their human counterparts; and how their own actions might affect the world, their task, and their human partners. Cognitive human–robot interaction is a research area that considers human(s), robot(s), and their joint actions as a cognitive system and seeks to create models, algorithms, and design guidelines to enable the design of such systems. Core research activities in this area include the development of representations and actions that allow robots to participate in joint activities with people; a deeper understanding of human expectations and cognitive responses to robot actions; and, models of joint activity for human–robot interaction. This chapter surveys these research activities by drawing on research questions and advances from a wide range of fields including computer science, cognitive science, linguistics, and robotics.

Aerial robotic vehicles are becoming a core field in mobile robotics. This chapter considers some of the fundamental modelling and control architectures in the most common aerial robotic platforms; small-scale rotor vehicles such as the quadrotor, hexacopter, or helicopter, and fixed wing vehicles. In order to control such vehicles one must begin with a good but sufficiently simple dynamic model. Based on such models, physically motivated control architectures can be developed. Such algorithms require realisable target trajectories along with real-time estimates of the system state obtained from on-board sensor suite. This chapter provides a first introduction across all these subjects for the quadrotor and fixed wing aerial robotic vehicles.

In this chapter we present an overview of the field of telerobotics with a focus on control aspects. To acknowledge some of the earliest contributions and motivations the field has provided to robotics in general, we begin with a brief historical perspective and discuss some of the challenging applications. Then, after introducing and classifying the various system architectures and control strategies, we emphasize bilateral control and force feedback. This particular area has seen intense research work in the pursuit of telepresence. We also examine some of the emerging efforts, extending telerobotic concepts to unconventional systems and applications. Finally,we suggest some further reading for a closer engagement with the field.

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.

This chapter first provides a formulation of the geometric path planning problem in Sect. 7.2 and then introduces sampling-based planning in Sect. 7.3. Sampling-based planners are general techniques applicable to a wide set of problems and have been successful in dealing with hard planning instances. For specific, often simpler, planning instances, alternative approaches exist and are presented in Sect. 7.4. These approaches provide theoretical guarantees and for simple planning instances they outperform samplingbased planners. Section 7.5 considers problems that involve differential constraints, while Sect. 7.6 overviews several other extensions of the basic problem formulation and proposed solutions. Finally, Sect. 7.8 addresses some important andmore advanced topics related to motion planning.

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.

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.