Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics. Analyzes the constant conflict between machine learning models and robot resources Presents a novel range estimation framework tested on real robots (custom built and commercially available)

Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics. - Analyzes the constant conflict between machine learning models and robot resources - Presents a novel range estimation framework tested on real robots (custom built and commercially available)

A revolutionary new framework that draws on insights from ecology for the design and analysis of long-duration robots Robots are increasingly leaving the confines of laboratories, warehouses, and manufacturing facilities, venturing into agriculture and other settings where they must operate in uncertain conditions over long timescales. This multidisciplinary book draws on the principles of ecology to show how robots can take full advantage of the environments they inhabit, including as sources of energy. Magnus Egerstedt introduces a revolutionary new design paradigm—robot ecology—that makes it possible to achieve long-duration autonomy while avoiding catastrophic failures. Central to ecology is the idea that the richness of an organism’s behavior is a function of the environmental constraints imposed by its habitat. Moving beyond traditional strategies that focus on optimal policies for making robots achieve targeted tasks, Egerstedt explores how to use survivability constraints to produce both effective and provably safe robot behaviors. He blends discussions of ecological principles with the development of control barrier functions as a formal approach to constraint-based control design, and provides an in-depth look at the design of the SlothBot, a slow and energy-efficient robot used for environmental monitoring and conservation. Visionary in scope, Robot Ecology presents a comprehensive and unified methodology for designing robots that can function over long durations in diverse natural environments.

This work supports a research program that seeks to radically improve the monitoring and physical collection of harmful algal blooms. The proposed system will outperform current manual, aerial, and satellite observations in both spatial and temporal resolution, via the coordination of unmanned aerial vehicles and unmanned surface vehicles in a collaborative heterogeneous robotic swarm system. In this thesis research, a low-cost highly scalable prototype for autonomously monitoring and mitigating the algal blooms is being developed and tested experimentally. New contributions include development and programming of a scalable low-cost agent-to-agent wireless communication platform. The unmanned surface vehicles are studied with different autonomous navigation patterns for target detection, to improve the overall process efficiency. Initial field experiments are performed with the autonomous boat platforms to establish ground truths on the hardware level.

Exploration is one of the main research topics in the field of mobile robotics. Multiple robots have been applied for different exploration algorithms In most of the these algorithms the robots exchange data via wireless communication assuming perfect data transmission in the whole environment. Unfortunately, this assumption does not always hold, as e.g. the range of wireless communication is limited. In this thesis Communicative Exploration is presented, which is based on Frontier-Based Exploration. For communicative exploration a group of robots is exploring unknown environments and maintain communication throughout the whole exploration process. The robots form a mobile ad-hoc network that is fully connected for the entire exploration. The motion of the robots is controlled via a heuristic utility function which assigns values to a population of configurations. The configuration with the highest utility value defines the motions of the robots. Different versions of communicative exploration are presented. In the first version a basestation is located outside the environment, from where a human operator can control or supervise the group of robots. During exploration the robots also maintain communication with the basestation. An extension to this version is presented where an improved motion control algorithm is implemented. By calculating a distance transform over the environment the robots are guided better towards the unknown areas of the environment. In a different version the basestation is omitted from the setup to enlarge the reachability of the robots. The group of robots explores the environment acting as a robot pack. Surprisingly, this version introduce situations where the robots get stuck in the environment in deadlock situations. Strategies are introduced that try to avoid or recover from these deadlocks.

Distributed robotics is an interdisciplinary and rapidly growing area, combining research in computer science, communication and control systems, and electrical and mechanical engineering. Distributed robotic systems can autonomously solve complex problems while operating in highly unstructured real-world environments. They are expected to play a major role in addressing future societal needs, for example, by improving environmental impact assessment, food supply, transportation, manufacturing, security, and emergency and rescue services. The goal of the International Symposium on Distributed Autonomous Robotic Systems (DARS) is to provide a forum for scientific advances in the theory and practice of distributed autonomous robotic systems. This volume of proceedings include 47 original contributions presented at the 13th International Symposium on Distributed Autonomous Robotic Systems (DARS 2016), which was held at the Natural History Museum in London, UK, from November 7th to 9th, 2016. The selected papers in this volume are authored by leading researchers from around the world, thereby providing a broad coverage and perspective of the state-of-the-art technologies, algorithms, system architectures, and applications in distributed robotic systems. The book is organized into seven parts, representative of critical long-term and emerging research thrusts in the multi-robot community: Distributed Coverage and Exploration; Multi-Robot Control; Multi-Robot Estimation; Multi-Robot Planning; Modular Robots and Smart Materials; Swarm Robotics; and Multi-Robot Systems in Applications.