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1. The swarm of 9 miniature blimps used to study flocking behaviour in an indoor settings using embedded infrared relative positioning sensors. Image courtesy of Chris Melhuish, Bristol Robotics Laboratory, University of Bristol and the West of England, Bristol.
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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...
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Abstract—Contactless communication and authentication in the RF-domain is still impaired from relay attacks. Countermeasures like distance bounding protocols are difficult to implement properly and are still continuously broken by sophisticated attacks. This work reviews a variety of approaches to counteract relay attacks by adding location-awarene...
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... When satellites fly in formation, they must keep a certain distance and orientation from one another at certain altitudes. Autonomous operations and ground-based control are two ways that can be used depending on the formation characteristics [24]. Ground control center gets the navigation details from the flying satellites which provide the requisite guidance for manoeuvring into proper formation positions. ...
... When satellites fly in formation, they must keep a certain distance and orientation from one another at certain altitudes. Autonomous operations and ground-based control are two ways that can be used depending on the formation characteristics [24]. Ground control center gets the navigation details from the flying satellites which provide the requisite guidance for manoeuvring into proper formation positions. ...
Satellite communication, in over half a century have attained remarkable breakthrough in high speed transmission. These innovations, on the other hand, have come in tandem with significant performance improvements in other IT and telecommunications systems. As a result, these significant benefits are not as visible to the broader public as they would be as if this burst of performance had occurred in isolation. In nutshell, space satellite communication using optical technology provide powerful alternative to radio frequency (RF) and make communication reliable and cost effective. Satellite communications systems allow a whole new race of civilian and defence operations in surveillance, telecommunications, object tracking and space exploration. Since independent satellites are restricted by volume, space and strength, small satellites production in bulk clusters may be effective for a range of wildfire monitoring, science missions, mapping in gravity and water management exploration, among others. The development of communications satellites would provide for a more comprehensive understanding of the near-earth environment as well as a more efficient and cost-effective way of reaching space, thanks to the utilisation of multi-satellite systems. As a result, when satellites are designing, IsOWC is an utmost point to be considered. The numerous researches being performed in the satellite communications for introducing multi communications based on different orbits are described in this study. IsOWC systems over radio frequency based communication are preferred due to large bandwidth, high speed, absence of electromagnetic interference (EMI), lower transmission losses and improved security. We also present a detailed listing of challenges in Is-OWC such as Doppler shift, point ahead angle, satellite vibration and tracking, acquisition tracking and pointing (ATP) and background noise sources.
... More recently, ref. [23,26,27] present several behaviors where a fully distributed deployment is achieved while creating a communication network. These systems do not require existing communication networks or a global localization system. ...
... Using the ideas presented in [23,[26][27][28][29], a UAV swarm can establish their own communications network. This network can be used for communicating swarm tasks or even to help other robots or a human team with subsequent work [5,10,11,24,30,31]. ...
This article will present two swarming behaviors for deployment in unstructured environments using unmanned aerial vehicles (UAVs). These behaviors will use stigmergy for communication. We found that there are currently few realistic deployment approaches that use stigmergy, due mainly to the difficulty of building transmitters and receivers for this type of communication. In this paper, we will provide the microscopic design of two behaviors with different technological and information requirements. We will compare them and also investigate how the number of agents influences the deployment. In this work, these behaviors will be exhaustively analyzed, taking into account different take-off time interval strategies, the number of collisions, and the time and energy required by the swarm. Numerous simulations will be conducted using unstructured maps generated at random, which will enable the establishment of the general functioning of the behaviors independently of the map used. Finally, we will show how both behaviors are capable of achieving the required deployment task in terms of covering time and energy consumed by the swarm. We will discuss how, depending on the type of map used, this task can be performed at a lower cost without using a more informed (but expensive) robotic swarm.
... Allred et al. (2007) used a ZigBee module to enable communication on a flock of fixed wing MAVs due to its combination of low energy consumption and long range (offering "a range of over 1 mile at 60 mW"). For comparisons of technical details of these technologies we refer the reader to the detailed book by Bensky (2019), the MAV-focused review by Zufferey et al. (2013), as well as the earlier comparisons by Lee et al. (2007). In addition to the technologies discussed above, there is also the possibility of enabling indirect communication via cellular networks. ...
This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.
... C OLLECTIVE motion of animal groups such as flocks of birds is an awe-inspiring natural phenomenon that has profound implications for the field of aerial swarm robotics [1], [2]. Animal groups in nature operate in a completely self-organized manner since the interactions between them are purely local. ...
Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.
... C OLLECTIVE motion of animal groups such as flocks of birds is an awe-inspiring natural phenomenon that has profound implications for the field of aerial swarm robotics [1], [2]. Animal groups in nature operate in a completely self-organized manner since the interactions between them are purely local. ...
Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.
... In addition, in many cases, the total or partial automation of the development process is sought. The literature of Evolutionary Robotics presents numerous cases of this type, often applied to UAVs [5], as a extension of the Evolutionary Robotics approach [6]. However, the drivers and strategies that are obtained through simulation are often troublesome when implemented in the real robot. ...
A mixed reality simulation framework is being developed as a tool to facilitate the elaboration, testing and deployment of control and collaborative strategies for teams of UAVs. The virtual world within the framework must contain a model of the phenomenon under analysis. It has been shown that, for complex cases, the use of real UAVs in an initiation phase could serve to simplify this model while increasing its accuracy. In a second step, a subsequent intermediate phase is implemented now. In this phase the virtual model is first scaled and then used to provide measurement data to the real planes that are equipped with virtual sensors in an augmented reality scenario. This way the cost and time of checking the coordination strategies and communications when several real planes are flying simultaneously can be greatly reduced. Once everything is tuned and adjusted within this intermediate phase, the whole system could be implemented in the full size real environment. An application on pollutant plume dispersion is used as a workbench case to show how this procedure is implemented in practice.
... In their design, they considered the new formation of the drones when a few of them malfunction or have other problems, such as engine failure [397]. In this situation, the other drones become aware of this problem and they find a new formation that allows the rest of the drones to collect the data which the damaged drone was supposed to collect [398]. Researchers from Ecole Polytechnique Federale de Lausanne University [398] developed swarm software for use in disaster situations. ...
... In this situation, the other drones become aware of this problem and they find a new formation that allows the rest of the drones to collect the data which the damaged drone was supposed to collect [398]. Researchers from Ecole Polytechnique Federale de Lausanne University [398] developed swarm software for use in disaster situations. They applied micro drones weighing in at 420 g each with a wing span of 80 cm. ...
Nowadays, there is a growing need for flying drones with diverse capabilities for both civilian and military applications. There is also a significant interest in the development of novel drones which can autonomously fly in different environments and locations and can perform various missions. In the past decade, the broad spectrum of applications of these drones has received most attention which led to the invention of various types of drones with different sizes and weights. In this review paper, we identify a novel classification of flying drones that ranges from unmanned air vehicles to smart dusts at both ends of this spectrum, with their new defined applications. Design and fabrication challenges of micro drones, existing methods for increasing their endurance, and various navigation and control approaches are discussed in details. Limitations of the existing drones, proposed solutions for the next generation of drones, and recommendations are also presented and discussed.
... They have applied micro drones weighing 420g each with a wingspan of 80cm. Their developed software uses a gyroscope along with two pressure sensors to make the decision of which flight path is better than another 9 . Various missions can be performed with more efficiency by applying multiple drones rather than a single one. ...
In this study, unmanned aerial vehicles (UAVs) were classified based on the principle of generation of lifting force. In addition, the structural characteristics, flight mechanisms, and research examples of each UAV category were introduced. Lifting force is the force that enables an aircraft to hover by countering gravity. It is one of the four forces (i.e., gravity, lifting force, thrust force, and drag force) that act on an aircraft while it flies. In this study, UAVs were classified into the following four categories based on the method of generation of lifting force: (1) fixed wing-based UAVs, which fly based on the lifting force generated indirectly from the forward thrust by using the geometry of aerodynamically designed fixed-wing cross-sections; (2) rotating and flapping wing-based UAVs, which generate lifting force directly using rotating or reciprocating wings, to counter gravity; (3) hybrid wing-based UAVs, which fly using both fixed and rotating wings; and (4) gas envelope-based UAVs, which generate lifting force using the difference in density between the gas and external air, rather than wings. These four types were classified further based specifically on the structural characteristics, and described using particular cases. Considering that UAVs have various flight purposes such as material transport, reconnaissance, surveillance, and special operations, it is anticipated that UAVs with the optimal flight mechanism for each purpose can be selected based on the flight characteristics of UAVs introduced in this paper.
