Nowadays, companies such as Amazon, Alibaba, and even pizza chains are pushing forward to use drones, also called UAVs (Unmanned Aerial Vehicles), for service provision, such as package and food delivery. As governments intend to use these immense economic benefits that UAVs have to offer, urban planners are moving forward to incorporate so-called UAV flight zones and UAV highways in their smart city designs. However, the high-speed mobility and behavior dynamics of UAVs need to be monitored to detect and, subsequently, to deal with intruders, rogue drones, and UAVs with a malicious intent.,,This paper proposes a UAV defense system for the purpose of intercepting and escorting a malicious UAV outside the flight zone. The proposed UAV defense system consists of a defense UAV swarm, which is capable to self-organize its defense formation in the event of intruder detection, and chase the malicious UAV as a networked swarm.,,Modular design principles have been used for our fully localized approach. We developed an innovative auto-balanced clustering process to realize the intercept- and capture-formation. As it turned out, the resulting networked defense UAV swarm is resilient against communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we show the feasibility and performance of our approach.
Regarding swarm control, research is conducted with flocking and swarming approaches @cite_15 . Some researchers use both terms interchangeably, while others distinguish between them. In @cite_3 , an overview is given on flocking and swarming algorithms. While the dynamics of flocking do not follow a pre-defined goal and therefore do not impose higher controllability levels, swarming can provide explicit elements of controllability to coordinate the swarm to the execution of a task, e.g., maneuvering to a specific tree, while avoiding obstacles on the way. Important work has been done by Reynolds, who introduced the standard model for swarm flocking @cite_8 .
@cite_15 @cite_3 @cite_8
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In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more elaborate sensing mechanisms to obtain the orientation of neighboring robots. So far, limited attention has been given to motion control, used to translate the output of these two control rules into robot motion. In this paper, we propose a novel motion control method: magnitude-dependent motion control (MDMC). Through simulations and real robot experiments, we show that, with MDMC, flocking in a random direction is possible without the need for alignment control and for robots having a preferred direction of travel. MDMC has the advantage to be implementable on very simple robots that lack the capability to detect the orientation of their neighbors. In addition, we introduce a small proportion of robots informed about a desired direction of travel. We compare MDMC with a motion control method used in previous robotics literature, which we call magnitude-independent motion control (MIMC), and we show that the swarms can travel longer distances in the desired direction when using MDMC instead of MIMC. Finally, we systematically study flocking under various conditions: with or without alignment control, with or without informed robots, with MDMC or with MIMC. Swarm formation and swarm flocking may conflict each other. Without explicit communication, such conflicts may lead to undesired topological changes since there is no global signal that facilitates coordinated and safe switching from one behavior to the other. Moreover, without coordination signals multiple swarm members might simultaneously assume leadership, and their conflicting leading directions are likely to prevent successful flocking. To the best of our knowledge, we present the first set of swarm flocking algorithms that maintain connectivity while electing direction for flocking, under conditions of no communication. The algorithms allow spontaneous direction requests and support direction changes. Control and coordination of a set of autonomous vehicles that can freely move on a plane is a widely studied topic in robotics. The focus on this kind of problem has grown in recent years because of the increased interest in studying systems populated by many, simple units, instead of few, powerful ones. In particular, these units simply observe the environment by using their sensors, and react following simple rules: the reaction to the environmental stimuli is called the behavior of the unit. In this paper we study the flocking problem : a set of mobile units are required to follow a leader unit while keeping a predetermined formation (i.e., they are required to move in flock, like a group of soldiers). Moreover, the units in the flock do not know beforehand the path the leader will take: their task is just to follow him wherever he goes, and to keep the formation while moving. In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors.