Energy-Efficient Indoor Search by Swarms of Simulated Flying Robots Without Global Information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints

Aerial collective systems

Deployment of multiple flying robots has attracted the interest of several research groups in the recent times both because such a feat represents many interesting scientific challenges and because aerial collective systems have a huge potential in terms of applications. By working together, multiple robots can perform a given task quicker or more efficiently than a single system. Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases. Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume. Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours. Flying in formation provides an effective way of decongesting the airspace. Aerial swarms also have an enormous artistic potential because they allow creating physical 3D structures that can dynamically change their shape over time. However, the challenges to actually build and control aerial swarms are numerous. First of all, a flying platform is often more complicated to engineer than a terrestrial robot because of the inherent weight constraints and the absence of mechanical link with any inertial frame that could provide mechanical stability and state reference. In the first section of this chapter, we therefore review this challenges and provide pointers to state-of-the-art methods to solve them. Then as soon as flying robots need to interact with each other, all sorts of problems arise such as wireless communication from and to rapidly moving objects and relative positioning. The aim of section 3 is therefore to review possible approaches to technically enable coordination among flying systems. Finally, section 4 tackles the challenge of designing individual controllers that enable a coherent behavior at the level of the swarm. This challenge is made even more difficult with flying robots because of their 3D nature and their motion constraints that are often related to the specific architectures of the underlying physical platforms. In this third section is complementary to the rest of this book as it focusses only on methods that have been designed for aerial collective systems

Indoor Navigation with a Swarm of Flying Robots

Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or long-range communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Make robots Be Bats: Specializing robotic swarms to the Bat algorithm

Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof. Xin-She Yang in 2010, with remarkable applications in industrial and scientific domains. However, to the best of authors' knowledge, this algorithm has never been applied so far in the context of swarm robotics. With the aim to fill this gap, this paper introduces the first practical implementation of the bat algorithm in swarm robotics. Our implementation is performed at two levels: a physical level, where we design and build a real robotic prototype; and a computational level, where we develop a robotic simulation framework. A very important feature of our implementation is its high specialization: all (physical and logical) components are fully optimized to replicate the most relevant features of the real microbats and the bat algorithm as faithfully as possible. Our implementation has been tested by its application to the problem of finding a target location within unknown static indoor 3D environments. Our experimental results show that the behavioral patterns observed in the real and the simulated robotic swarms are very similar. This makes our robotic swarm implementation an ideal tool to explore the potential and limitations of the bat algorithm for real-world practical applications and their computer simulations.This research has been kindly supported by the Computer Science National Program of the Spanish Research Agency (Agencia Estatal de Investigación) and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), the project EVOLFORMAS Ref. #JU12, jointly supported by public body SODERCAN of the Regional Government of Cantabria and the European funds FEDER, the project PDE-GIR of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions grant agreement #778035, Toho University (Funabashi, Japan), and the University of Cantabria (Santander, Spain). The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. Special thanks are also due to the Editors and the three anonymous reviewers for their encouraging and constructive comments and very helpful feedback that allowed us to improve our paper signi cantly

Swarms of Unmanned Aerial Vehicles – A Survey

The purpose of this study is to focus on the analysis of the core characteristics of swarms of drones or Unmanned Aerial Vehicles and to present them in a way that facilitates analysis of public awareness on such swarms. Furthermore, the functionality, problems, and importance of drones are highlighted. Lastly, the experimental survey from a bunch of academic population demonstrates that the swarms of drones are fundamental future agendas and will be adapted by the time.</p