4,012 research outputs found

    UAS Path Planning for Dynamical Wildfire Monitoring with Uneven Importance

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    Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. When planning the path of an UAS for wildfire monitoring, it is important to consider the uneven propagation nature of the wildfire because different parts of the fire boundary demand different levels of monitoring attention (importance) based on the propagation speed. In addition, many of the existing works adopt a centralized approach for the path planning of the UASs. However, the use of centralized approaches is often limited in terms of applicability and adaptability. This work focuses on developing decentralized UAS path planning algorithms to autonomously monitor a spreading wildfire considering uneven importance. The algorithms allow the UASs to focus on the most active regions of a wildfire while still covering the entire fire perimeter. When monitoring a relatively smaller and spatially static fire, a single UAS might be adequate for the task. However, when monitoring a larger wildfire that is evolving dynamically in space and time, efficient and optimized use of multiple UASs is required. Based on this need, we also focus on decentralized and importance-based multi-UAS path planning for wildfire monitoring. The design, implementation, analysis, and simulation results have been discussed in details for both single-UAS and multi-UAS path planning algorithms. Experiment results show the effectiveness and robustness of the proposed algorithms for dynamic wildfire monitoring

    Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires

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    Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown characteristics, such as size, scale, and propagation velocity, and to plan accordingly. In this paper, we propose a distributed control framework to coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered active sensing of wildfires. We develop a dual-criterion objective function based on Kalman uncertainty residual propagation and weighted multi-agent consensus protocol, which enables the UAVs to actively infer the wildfire dynamics and parameters, track and monitor the fire transition, and safely manage human firefighters on the ground using acquired information. We evaluate our approach relative to prior work, showing significant improvements by reducing the environment's cumulative uncertainty residual by more than 102 10^2 and 105 10^5 times in firefront coverage performance to support human-robot teaming for firefighting. We also demonstrate our method on physical robots in a mock firefighting exercise

    Using a Semi-autonomous Drone Swarm to Support Wildfire Management – A Concept of Operations Development Study

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    This paper provides insights into a human factors-oriented Concept of Operations (ConOps), which can be applied for future semi-autonomous drone swarms to support the management of wildfires. The results provide, firstly, an overview of the current practices to manage wildfires in Finland. Secondly, some of the current challenges and future visions about drone usage in a wildfire situation are presented. Third, a description of the key elements of the developed future ConOps for operating a drone swarm to support the combat of wildfires is given. The ConOps has been formulated based on qualitative research, which included a literature review, seven subject matter expert interviews and a workshop with 40 professionals in the domain. Many elements of this ConOps may also be applied to a variety of other swarm robotics operations than only wildfire management. Finally, as the development of the ConOps is still in its first stage, several further avenues for research and development are proposed

    Aggregate Drone Monitoring of Wildfires

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    Wildfires are an ever-increasing problem, exacerbated by the current global warming trends. Accordingly, it is becoming more and more relevant to monitor factors influencing their outbreaks and spreading to preemptively act on the riskiest areas and guide interventions in case an outbreak occurs. Different approaches have been proposed during the decades tackling this issue, which however require large datasets that are difficult and expensive to gather. In this paper, we propose to address the management of wildfires by empowering existing centralised models with a decentralised component. Leveraging dedicated monitoring drones together with smartphones held by experts and intervention corps, a decentralised system could both enhance data collection and assist interventions. As conditions near wildfires require strong fault-tolerance guarantees, we propose to develop such an application through aggregate programming, a novel approach to the resilient programming of decentralised systems

    THE NEXT GENERATION OF WILDLAND FIREFIGHTING TOOLS: USING UAV SWARMS FOR FIRE ATTACK

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    Wildland fires pose a direct threat to homeland security because of the severe personal, economic, and social stress they cause to those affected. As unmanned aerial vehicle (UAV) swarms become more ubiquitous in use, they will likely find a place as a frontline firefighting aerial asset, increasing the operational pace of aerial suppression flights and consequently increasing the safety of firefighters. This thesis explored the concept of using UAV swarms as a method for fire attack by comparing theoretical swarms to a conventional aerial asset within a realistic fire scenario and then using a systems engineering approach to define pressure points for implementing UAV swarms in the wildland space. The findings of this research support continued development of UAV swarms and clearly define areas that must be addressed before implementing large-scale UAV swarm flights. The firefighting UAV swarm system shows great promise due to its relative portability and ability to provide an aerial firefighting option to areas without ready access to conventional firefighting aircraft. It will be critical, however, to address logistical and communications constraints of UAV swarm systems before implementation to ensure positive outcomes.Civilian, Portland Fire and RescueApproved for public release. Distribution is unlimited
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