Preface

The area of unmanned robotic systems is one of the fastest growing industries and has a number of evolving applications. Autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots to find a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control. Researchers have developed intelligent control algorithms for the swarms after deep study of animal behavior in herds, bird flocks, and fish schools.

In the field of robotics, the use of Unmanned Aerial Vehicles (UAVs), more commonly known as drones, has drastically increased over the recent years. In particular, there is a special surge of interest in quadrotor drones because of their advantages over fixed-wing drones in terms of their maneuverability and versatility. Moreover, quadrotor drones have a straightforward mechanical design, are relatively cheap to purchase, and are small in size. Quadrotors are widely used in military and civilian applications involving search and rescue, area mapping, surveillance, wildlife protection, and infrastructure inspection. All these applications involve visual mapping of large areas. The popularity of UAVs to perform these tasks is supported by increased technological possibilities in the area of image recognition. UAVs are strongly coupled, inherently nonlinear systems that require advanced nonlinear control techniques.

Strategies employed for the control of UAVs include linearization-based control techniques such as PID and LQR, and nonlinear control methods. The search for increased performance has always been the main goal for control engineers. As multirotor UAVs are highly nonlinear systems, simple control strategies may not suffice for the performance demand. Due to the high degree of nonlinearity, parameter identification can be difficult, which raises uncertainties in the system model. To cope with these model uncertainties, robustness of the control law becomes a necessity. Sliding mode control is a nonlinear control tool and is known to be a robust control technique. To achieve reliable quadcopter control over a wider operational envelope, several nonlinear control methods have recently been developed. Popular nonlinear control methods for quadrotor systems include backstepping, feedback linearization, dynamic inversion, adaptive control, Lyapunov-based robust control, passivity-based control, fuzzy-model approach, and sliding mode control.

This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Chapter 1 emphasizes robotic search and rescue via in-pipe inspection robots. It gives an overview of a screw-drive in-pipe mobile robot, a three-module parallel arrangement type in-pipe mobile robot, and several types of multi-link articulated wheeled-type in-pipe robots. Chapter 2 is devoted to the problem of autonomous coordinated search by an interconnected group of moving robots for the purpose of find a source of hazardous emissions such as hazardous gas and particles. The chapter introduces a search strategy that

operates in a completely decentralized manner, as long as the communication network of the moving robots forms a connected graph. Chapter 3 proposes a vision-based sliding mode control algorithm for autonomous landing of a quadrotor UAV. The effectiveness of the control algorithm is illustrated through experimental results obtained using a DJI Matrice M100 drone. Chapter 4 presents a custom-built 3D laser range platform SWAP and compares it against an architectural laser scanner. The main advantage of the platform is its ability to scan in a continuous mode. The chapter introduces a new mapping tool (mapit) that can support and automate the registration of large sets of point clouds. Finally, Chapter 5 summarizes different advanced control techniques for UAV control. These techniques include backstepping, feedback linearization, and sliding mode control. A commonly known UAV nonlinear model is presented and the proposed control strategies have been implemented using MATLAB. Simulation results are included to demonstrate the effectiveness of these control techniques.
