410 research outputs found
Emergent velocity agreement in robot networks
In this paper we propose and prove correct a new self-stabilizing velocity
agreement (flocking) algorithm for oblivious and asynchronous robot networks.
Our algorithm allows a flock of uniform robots to follow a flock head emergent
during the computation whatever its direction in plane. Robots are
asynchronous, oblivious and do not share a common coordinate system. Our
solution includes three modules architectured as follows: creation of a common
coordinate system that also allows the emergence of a flock-head, setting up
the flock pattern and moving the flock. The novelty of our approach steams in
identifying the necessary conditions on the flock pattern placement and the
velocity of the flock-head (rotation, translation or speed) that allow the
flock to both follow the exact same head and to preserve the flock pattern.
Additionally, our system is self-healing and self-stabilizing. In the event of
the head leave (the leading robot disappears or is damaged and cannot be
recognized by the other robots) the flock agrees on another head and follows
the trajectory of the new head. Also, robots are oblivious (they do not recall
the result of their previous computations) and we make no assumption on their
initial position. The step complexity of our solution is O(n)
MacroSwarm: A Field-based Compositional Framework for Swarm Programming
Swarm behaviour engineering is an area of research that seeks to investigate
methods and techniques for coordinating computation and action within groups of
simple agents to achieve complex global goals like pattern formation,
collective movement, clustering, and distributed sensing. Despite recent
progress in the analysis and engineering of swarms (of drones, robots,
vehicles), there is still a need for general design and implementation methods
and tools that can be used to define complex swarm behaviour in a principled
way. To contribute to this quest, this article proposes a new field-based
coordination approach, called MacroSwarm, to design and program swarm behaviour
in terms of reusable and fully composable functional blocks embedding
collective computation and coordination. Based on the macroprogramming paradigm
of aggregate computing, MacroSwarm builds on the idea of expressing each swarm
behaviour block as a pure function mapping sensing fields into actuation goal
fields, e.g. including movement vectors. In order to demonstrate the
expressiveness, compositionality, and practicality of MacroSwarm as a framework
for collective intelligence, we perform a variety of simulations covering
common patterns of flocking, morphogenesis, and collective decision-making
Deaf, Dumb, and Chatting Robots, Enabling Distributed Computation and Fault-Tolerance Among Stigmergic Robot
We investigate ways for the exchange of information (explicit communication)
among deaf and dumb mobile robots scattered in the plane. We introduce the use
of movement-signals (analogously to flight signals and bees waggle) as a mean
to transfer messages, enabling the use of distributed algorithms among the
robots. We propose one-to-one deterministic movement protocols that implement
explicit communication. We first present protocols for synchronous robots. We
begin with a very simple coding protocol for two robots. Based on on this
protocol, we provide one-to-one communication for any system of n \geq 2 robots
equipped with observable IDs that agree on a common direction (sense of
direction). We then propose two solutions enabling one-to-one communication
among anonymous robots. Since the robots are devoid of observable IDs, both
protocols build recognition mechanisms using the (weak) capabilities offered to
the robots. The first protocol assumes that the robots agree on a common
direction and a common handedness (chirality), while the second protocol
assumes chirality only. Next, we show how the movements of robots can provide
implicit acknowledgments in asynchronous systems. We use this result to design
asynchronous one-to-one communication with two robots only. Finally, we combine
this solution with the schemes developed in synchronous settings to fit the
general case of asynchronous one-to-one communication among any number of
robots. Our protocols enable the use of distributing algorithms based on
message exchanges among swarms of Stigmergic robots. Furthermore, they provides
robots equipped with means of communication to overcome faults of their
communication device
Networking the Boids is More Robust Against Adversarial Learning
Swarm behavior using Boids-like models has been studied primarily using
close-proximity spatial sensory information (e.g. vision range). In this study,
we propose a novel approach in which the classic definition of
boids\textquoteright \ neighborhood that relies on sensory perception and
Euclidian space locality is replaced with graph-theoretic network-based
proximity mimicking communication and social networks. We demonstrate that
networking the boids leads to faster swarming and higher quality of the
formation. We further investigate the effect of adversarial learning, whereby
an observer attempts to reverse engineer the dynamics of the swarm through
observing its behavior. The results show that networking the swarm demonstrated
a more robust approach against adversarial learning than a local-proximity
neighborhood structure
Design of an UAV swarm
This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation
Swarm-based counter UAV defense system
Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach
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