Using tornado-related weather data to route unmanned aerial vehicles to locate damage and victims

Grogan, Sean; Pellerin, Robert; Gamache, Michel

doi:10.1007/s00291-021-00640-1

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…To solve the problem, the authors presented a heuristic approach based on hybrid large neighborhood search. In a recent paper, Grogan et al [14] addressed a drone application for relief operations after tornado. The authors presented a multi-depot routing problem to minimize the maximum route duration while imposing a maximum drone endurance limit for each drone.…”

Section: Literature Reviewmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

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In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver.

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Section: Literature Reviewmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

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“…In a recent paper, Grogan et al [9] addressed a drone application for relief operations conducted after a tornado. The authors proposed a routing problem considering the maximum drone endurance limit with the aim of minimizing the maximum route duration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…point of view, is represented by Unmanned Aerial Vehicles (UAVs), also known as drones. The scope of applications for UAVs technology is quite broad ranging from target hunting [5], wildfires monitoring [6], military and civil surveillance [7] to post-disaster management and commercial deliveries [8], [9], [10], [11], [12].…”

mentioning

confidence: 99%

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

2023

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This paper studies the drone-aided last-mile delivery problem with shared depot resources. Our research motivation comes from E-commerce logistics, where big companies such as Amazon, are already filing up patents for the development of drone-friendly fulfillment centers towers that could serve as both charging hubs and convenient pit stops for delivery drones to pick up and drop off packages efficiently. We mainly focus on the tactical decisions about the selection of shared fulfillment centers used as the drone launch and retrieve stations and the fleet size plans. The operational drone route decisions are also incorporated into a unified framework to account for the mutual impact between tactical and operational plans. Moreover, we consider explicitly the non-linear and load-dependent nature of the energy consumption function for drone batteries. The problem is formulated as a mixed integer program with linear constraints, developed in the realm of layered networks, where the non-linear nature of energy consumption and its load dependency are incorporated and efficiently handled without the need of approximating non-linear terms. The proposed model is tested on an extensive set of instances with up to 75 customers, showing its computational efficiency. Insights about the route costs and spatial configuration of depots are also discussed.

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“…Autonomous robots play an important role in emergency response missions (Luneckas et al, 2021), including search and rescue missions after natural or human‐made disasters (Grogan et al, 2018; Murphy, 2012) and interventions during emergencies, like, chemical, biological, radiological, and nuclear (Guzman et al, 2016) and wildfires (Baudoin et al, 2009). They are used for the first response in disaster scenarios to gather the information required for later human intervention (Baudoin et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

An autonomous ground robot to support firefighters' interventions in indoor emergencies

Talavera

Roldán‐Gómez

Martín

et al. 2023

Journal of Field Robotics

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Intervention teams act in hostile scenarios where reducing mission times and accident risks is critical. In these situations, the availability of accurate information about the environment plays a key role in ensuring the well-being of rescuers and victims. This information required to plan the interventions in indoor emergencies encompasses the location of fires and the presence of dangerous gases. Robotics and remote sensing technologies can help emergency teams to obtain this information in real-time without exposing themselves. Additionally, the accurate simulation of the environments allows the teams to plan their interventions, creating routes to safely access the affected areas and evacuate the victims. This article presents a robotic solution developed to satisfy the demands of intervention teams. More specifically, it describes an autonomous ground robot that can obtain real-time location and environmental data from indoor fires, as well as a simulator that reproduces these emergency scenarios and facilitates mission planning.In this way, emergency teams can know the conditions in the scenario before, during, and after the intervention. Thus, risks are minimized by improving their situational awareness and reducing their exposure times during the mission. The system has been developed and validated in collaboration with the end-users and under realistic harsh environments.During these experiments, the robot was used to detect fire sources and cold smoke and provide environmental information to firefighters. Additionally, the simulator provided alternative routes for accessing and exiting the scene faster and safer by dodging potentially dangerous areas.

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Using tornado-related weather data to route unmanned aerial vehicles to locate damage and victims

Cited by 17 publications

References 27 publications

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

An autonomous ground robot to support firefighters' interventions in indoor emergencies

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