Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning

Faied, Mariam; Mostafa, Ahmed Karam Abdelfattah; Girard, Anouck

doi:10.2514/6.2010-8435

Cited by 20 publications

(11 citation statements)

References 19 publications

(22 reference statements)

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“…In general vehicle routing problems, the standard objective function is typically time minimization for visiting a set number of nodes. In UAV fleet mission planning, several variants of objective functions such as reducing individual UAV costs, increasing safety in operations, reducing lead time, and increasing the load capacity of the entire system are considered [3,[21][22][23][24]. Furthermore, the problem can be considered an extension of the vehicle routing and scheduling problems and belongs to the class of NP-hard problems [2,4].…”

Section: Literature Reviewmentioning

confidence: 99%

UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

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Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

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Section: Literature Reviewmentioning

confidence: 99%

UAV Mission Planning Resistant to Weather Uncertainty

Thibbotuwawa

Bocewicz

Radzki

et al. 2020

Sensors

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“…T w here represents the time window. For example, for location-2 we have a time window [1][2][3][4][5] which means that an agent cannot be somewhere else between time step 1 and 5. Because of that, we can not assign UAVs 1, 2 and 3 to location-1, as it's time window collides with the time window of location-1.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…However, for some cases, classical VRP formulation is inadequate to represent real-world scenarios. Because of that VRP has several variations, Faied et al [2] provided a survey about these variations. One variation of VRP is capacitated vehicle routing problem (CVRP).…”

Section: Introductionmentioning

confidence: 99%

Solving the area coverage problem with UAVs: A vehicle routing with time windows variation

Semiz

Polat

2020

Robotics and Autonomous Systems

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In real life, providing security for a set of large areas by covering the area with Unmanned Aerial Vehicles (UAVs) is a difficult problem that consist of multiple objectives. These difficulties are even greater if the area coverage must continue throughout a specific time window. We address this by considering a Vehicle Routing Problem with Time Windows (VRPTW) variation in which capacity of agents is one and each customer (target area) must be supplied with more than one vehicles simultaneously without violating time windows. In this problem, our aim is to find a way to cover all areas with the necessary number of UAVs during the time windows, minimize the total distance traveled, and provide a fast solution by satisfying the additional constraint that each agent has limited fuel. We present a novel algorithm that relies on clustering the target areas according to their time windows, and then incrementally generating transportation problems with each cluster and the ready UAVs. Then we solve transportation problems with the simplex algorithm to generate the solution. The performance of the proposed algorithm and other implemented algorithms to compare the solution quality is evaluated on example scenarios with practical problem sizes.

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“…Studies in the literature have used methods for UAV mission planning where the routes have been derived by satisfying the UAV dynamics using linear approximations for energy-consumption models [25,26]. Certain existing models have not considered all the significant physical properties related to UAVs and have used approaches such as VRP with time windows (VRPTW) [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

et al. 2019

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With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.

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Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning

Cited by 20 publications

References 19 publications

UAV Mission Planning Resistant to Weather Uncertainty

UAV Mission Planning Resistant to Weather Uncertainty

Solving the area coverage problem with UAVs: A vehicle routing with time windows variation

A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

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