Modeling Control State Estimation And Path Planning Methods For Autonomous Multirotor Aerial Robots

This review paper aims to provide an overview of core modeling, control, estimation, and planning concepts and approaches for micro aerial robots of the rotorcraft class. A comprehensive description of a set of methods that enable automated flight control, state estimation in GPS–denied environments, as well as path planning techniques for autonomous exploration is provided, and serves as a holistic point of reference for those interested in the field of unmanned aerial systems. Further discussion for other applications of aerial robots concludes this manuscript.

Discover the latest research in path planning and robust path tracking control In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models Practical discussions of path generation and path modeling available in current literature In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, Autonomous Road Vehicle Path Planning and Tracking Control is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

This text is a thorough treatment of the rapidly growing area of aerial manipulation. It details all the design steps required for the modeling and control of unmanned aerial vehicles (UAV) equipped with robotic manipulators. Starting with the physical basics of rigid-body kinematics, the book gives an in-depth presentation of local and global coordinates, together with the representation of orientation and motion in fixed- and moving-coordinate systems. Coverage of the kinematics and dynamics of unmanned aerial vehicles is developed in a succession of popular UAV configurations for multirotor systems. Such an arrangement, supported by frequent examples and end-of-chapter exercises, leads the reader from simple to more complex UAV configurations. Propulsion-system aerodynamics, essential in UAV design, is analyzed through blade-element and momentum theories, analysis which is followed by a description of drag and ground-aerodynamic effects. The central part of the book is dedicated to aerial-manipulator kinematics, dynamics, and control. Based on foundations laid in the opening chapters, this portion of the book is a structured presentation of Newton–Euler dynamic modeling that results in forward and backward equations in both fixed- and moving-coordinate systems. The Lagrange–Euler approach is applied to expand the model further, providing formalisms to model the variable moment of inertia later used to analyze the dynamics of aerial manipulators in contact with the environment. Using knowledge from sensor data, insights are presented into the ways in which linear, robust, and adaptive control techniques can be applied in aerial manipulation so as to tackle the real-world problems faced by scholars and engineers in the design and implementation of aerial robotics systems. The book is completed by path and trajectory planning with vision-based examples for tracking and manipulation.

A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.

Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.

Building on the successful first and second volumes, this book is the third volume of the Springer book on the Robot Operating System (ROS): The Complete Reference. The Robot Operating System is evolving from year to year with a wealth of new contributed packages and enhanced capabilities. Further, the ROS is being integrated into various robots and systems and is becoming an embedded technology in emerging robotics platforms. The objective of this third volume is to provide readers with additional and comprehensive coverage of the ROS and an overview of the latest achievements, trends and packages developed with and for it. Combining tutorials, case studies, and research papers, the book consists of sixteen chapters and is divided into five parts. Part 1 presents multi-robot systems with the ROS. In Part 2, four chapters deal with the development of unmanned aerial systems and their applications. In turn, Part 3 highlights recent work related to navigation, motion planning and control. Part 4 discusses recently contributed ROS packages for security, ROS2, GPU usage, and real-time processing. Lastly, Part 5 deals with new interfaces allowing users to interact with robots. Taken together, the three volumes of this book offer a valuable reference guide for ROS users, researchers, learners and developers alike. Its breadth of coverage makes it a unique resource.

Drones have an increasing place in numerous applications already started to take advantage from those, in particular in the fields of photography and video making, or simply for leisure activities. Simultaneously, the picture of autonomous aerial robots widely spread as a mark of innovation, such that many civilian of industrial applications are now envisioned through this aspect. One could cite, for instance, the persistent idea of aerial home delivery of goods, exploited by many companies. Another spread use-case is the deployment of fleets of aerial robots for monitoring activities, in hard-to-access environments, such as high mountains.The aerial robotics research community is active from numerous years, and the state of the art keeps improving, being through the conception of novel, more adaptive control algorithms, or the improvements of the hardware designs, opening new ranges of possibilities.The deployment of such robots in the scope of applications in uncontrolled environments comes with a lot of challenges, in particular regarding the perception of the surroundings. Exteroceptive sensors are indeed mandatory for most of autonomous applications. Among those sensors, cameras hold a peculiar position.It is on the one hand due to the simple onboard integration with their small size and weight,and on the other hand to the design of human-made environments, which are heavily built around visual markers (signs, illuminated signals...) However, maintaining visibility over objects or phenomenon often collide with the motion requirements of the robot, or with the tasks to which it is assigned. This effect is prominent when using underactuated robots, which are the most widely spread types of aerial vehicles, partly because of their higher energy efficiency. This property implies a strong coupling between position and orientation: the robot needs to tilt to move, and corollary moves when it tilts, thus altering the sensor bearing.From this assessment, the robotics community works to produce sensorimotor algorithms, able to produce motions while accounting for perception.This thesis takes place in this context, aiming at proposing such control methods to enforce the visibility over a phenomenon of interest through the onboard sensors. Moreover, to ensure the feasibility of the generated commands, it is required to account for the various actuation limitations of the robots. Finally, this thesis devotes to propose generic formulations, thus avoiding to propose ad hoc solutions, which would be contingent to a specific problem.To tackles these aspects under a common formalism, the proposed solutions are based on optimal and predictive control policies. These are based on numerical optimization, implying the need of accurate models, and thus accounting for the system nonlinearities, which are often disregarded for simplification.The contributions of this these are the aggregation of the various concepts in a common paradigm,and the formalization of the various mathematical functions transcribing the objectives and constraints related to perception. This paradigm is used in the scope of several applications related to usual perception-driven tasks in aerial robotics, namely the tracking of dynamic phenomenon, the improvement of this tracking, or the visual-inertial localization. Finally, the proposed solutions are implemented and tested in simulations and on real aerial robots.The work conducted throughout this thesis led to various publications in international peer-reviewed conferences and journals. All the related software production from these works are published open-source for the robotics community.

This second volume is a continuation of the successful first volume of this Springer book, and as well as addressing broader topics it puts a particular focus on unmanned aerial vehicles (UAVs) with Robot Operating System (ROS). Consisting of three types of chapters: tutorials, cases studies, and research papers, it provides comprehensive additional material on ROS and the aspects of developing robotics systems, algorithms, frameworks, and applications with ROS. ROS is being increasingly integrated in almost all kinds of robots and is becoming the de-facto standard for developing applications and systems for robotics. Although the research community is actively developing applications with ROS and extending its features, amount of literature references is not representative of the huge amount of work being done. The book includes 19 chapters organized into six parts: Part 1 presents the control of UAVs with ROS, while in Part 2, three chapters deal with control of mobile robots. Part 3 provides recent work toward integrating ROS with Internet, cloud and distributed systems. Part 4 offers five case studies of service robots and field experiments. Part 5 presents signal-processing tools for perception and sensing, and lastly, Part 6 introduces advanced simulation frameworks. The diversity of topics in the book makes it a unique and valuable reference resource for ROS users, researchers, learners and developers.