This book describes the resilient navigation techniques under the background of collaboration in swarm. The significance of this work is that it focuses on the navigation enhancement by collaboration in swarm rather than ground infrastructure, which exploit potentialities of swarm in GNSS restricted environment. Although unmanned swarm is receiving greater attention, both through theoretical research and through increasing mention in the industrial developments, the navigation promotion by effective and efficient collaboration remains largely unexplored. While my scholarly work has explored some of the modeling, error characteristic, fusion algorithm, fault detection, and isolation aspects of the “adaptive navigation system” (such as the navigation system of robots and ground vehicles, aircrafts, aerospace vehicles, and unmanned aerial vehicles), the present book proposes the specialized investigation on the navigation with the resilient character, which could maintain the performance by essential collaboration with members in swarm in GNSS degradation environment. This book focused on the resilient navigation techniques under the background of collaboration in swarm. The key techniques of collaborative resilient navigation are proposed, including the collaboration framework, collaborative observation modeling, geometry optimization, integrity augmentation, and fault detection. The experiments are also carried out to validate the effectiveness of the corresponding techniques.

Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

The acclaimed bestseller that's teaching the world about the power of mass collaboration. Translated into more than twenty languages and named one of the best business books of the year by reviewers around the world, Wikinomics has become essential reading for business people everywhere. It explains how mass collaboration is happening not just at Web sites like Wikipedia and YouTube, but at traditional companies that have embraced technology to breathe new life into their enterprises. This national bestseller reveals the nuances that drive wikinomics, and share fascinating stories of how masses of people (both paid and volunteer) are now creating TV news stories, sequencing the human gnome, remixing their favorite music, designing software, finding cures for diseases, editing school texts, inventing new cosmetics, and even building motorcycles.

Presents a unified treatment of HRI-related issues, identifies key themes, and discusses challenge problems that are likely to shape the field in the near future. The survey includes research results from a cross section of the universities, government efforts, industry labs, and countries that contribute to HRI.

This book draws inspiration from natural shepherding, whereby a farmer utilizes sheepdogs to herd sheep, to inspire a scalable and inherently human friendly approach to swarm control. The book discusses advanced artificial intelligence (AI) approaches needed to design smart robotic shepherding agents capable of controlling biological swarms or robotic swarms of unmanned vehicles. These smart shepherding agents are described with the techniques applicable to the control of Unmanned X Vehicles (UxVs) including air (unmanned aerial vehicles or UAVs), ground (unmanned ground vehicles or UGVs), underwater (unmanned underwater vehicles or UUVs), and on the surface of water (unmanned surface vehicles or USVs). This book proposes how smart ‘shepherds’ could be designed and used to guide a swarm of UxVs to achieve a goal while ameliorating typical communication bandwidth issues that arise in the control of multi agent systems. The book covers a wide range of topics ranging from the design of deep reinforcement learning models for shepherding a swarm, transparency in swarm guidance, and ontology-guided learning, to the design of smart swarm guidance methods for shepherding with UGVs and UAVs. The book extends the discussion to human-swarm teaming by looking into the real-time analysis of human data during human-swarm interaction, the concept of trust for human-swarm teaming, and the design of activity recognition systems for shepherding. Presents a comprehensive look at human-swarm teaming; Tackles artificial intelligence techniques for swarm guidance; Provides artificial intelligence techniques for real-time human performance analysis.