1,429 research outputs found
Towards Evolving Cooperative Mapping for Large-Scale UAV Teams
A team of UAVs has great potential to handle real-world challenges. Knowing the environment is essential to perform in an effective manner. However, in many situations, a map of the environment will not be available. Additionally, for autonomous systems, it is necessary to have approaches that require little energy, computing, power, weight and size. To address this, we propose a light-weight, evolving, and memory efficient cooperative approach for estimating the map of an environment with a team of UAVs. Additionally, we present proof-of-concept experiments with real-life flights, showing that we can estimate maps using an off-the-shelf web-camera
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
A Framework for Automatic Behavior Generation in Multi-Function Swarms
Multi-function swarms are swarms that solve multiple tasks at once. For
example, a quadcopter swarm could be tasked with exploring an area of interest
while simultaneously functioning as ad-hoc relays. With this type of
multi-function comes the challenge of handling potentially conflicting
requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites
in combination with a suitable controller structure, a framework for automatic
behavior generation in multi-function swarms is proposed. The framework is
tested on a scenario with three simultaneous tasks: exploration, communication
network creation and geolocation of RF emitters. A repertoire is evolved,
consisting of a wide range of controllers, or behavior primitives, with
different characteristics and trade-offs in the different tasks. This
repertoire would enable the swarm to transition between behavior trade-offs
online, according to the situational requirements. Furthermore, the effect of
noise on the behavior characteristics in MAP-elites is investigated. A moderate
number of re-evaluations is found to increase the robustness while keeping the
computational requirements relatively low. A few selected controllers are
examined, and the dynamics of transitioning between these controllers are
explored. Finally, the study develops a methodology for analyzing the makeup of
the resulting controllers. This is done through a parameter variation study
where the importance of individual inputs to the swarm controllers is assessed
and analyzed
UAV flight coordination for communication networks:Genetic algorithms versus game theory
The autonomous coordinated flying for groups of unmanned aerial vehicles that maximise network coverage to mobile ground-based units by efficiently utilising the available on-board power is a complex problem. Their coordination involves the fulfilment of multiple objectives that are directly dependent on dynamic, unpredictable and uncontrollable phenomena. In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology. Genetic algorithms and non-cooperative games are employed for the generation of optimal flying solutions. The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts. Coupled with a communication model that addresses environmental conditions, they optimise flying to maximising the number of supported ground-based units. Results of large-scale scenarios highlight the ability of genetic algorithms to evolve flexible sets of manoeuvres that keep the flying vehicles separated and provide optimal solutions over shorter settling times. In comparison, game theory is found to identify strategies of predefined manoeuvres that maximise coverage but require more time to converge
Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
Motivated by the tremendous progress we witnessed in recent years, this paper
presents a survey of the scientific literature on the topic of Collaborative
Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM.
With fleets of self-driving cars on the horizon and the rise of multi-robot
systems in industrial applications, we believe that Collaborative SLAM will
soon become a cornerstone of future robotic applications. In this survey, we
introduce the basic concepts of C-SLAM and present a thorough literature
review. We also outline the major challenges and limitations of C-SLAM in terms
of robustness, communication, and resource management. We conclude by exploring
the area's current trends and promising research avenues.Comment: 44 pages, 3 figure
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