Unmanned aerial vehicle routing in the presence of threats

Alotaibi, Kamil A.; Rosenberger, Jay M.; Mattingly, Stephen P.; Punugu, Raghavendra Kumar; Visoldilokpun, Siriwat

doi:10.1016/j.cie.2017.10.030

Cited by 29 publications

(21 citation statements)

References 66 publications

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“…The reconnaissance task for short-range tactical UAVs is hardly present in the literature, although the route planning algorithms, based on VRPTW models, are described in many papers [6][7][8][9][10][11][12][13]. However, the basic issues for these algorithms are ignored, such as the method of constructing the connection network, which takes into account the sensor capabilities, the arrangement of targets to be identified, and threats to UAVs in the terrain.…”

Section: Introductionmentioning

confidence: 99%

“…When describing a planning process for UAV mission, modern literature addresses the problems of planning UAV flight routes to accomplish their tasks and to avoid threats generated by enemy forces, such as radars or missile systems [6,24]. Many articles in the literature have analyzed UAV route planning in a hostile area of activity and they attempted to assess the risks associated with UAV routes.…”

Section: Introductionmentioning

confidence: 99%

“…The risk may be caused by sources of danger or obstacles such as enemy ground-to-air missiles, radar detection zones, no-fly zones, terrain, or airborne vehicles with which UAVs may collide on their routes. Threats can be stationary or mobile, although the majority of articles on this topic concern stationary threats [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Generate the net S S S where vertices belong to the set W W W = F F F ∪ V V V using algorithms 1 and 2 6:…”

mentioning

confidence: 99%

“…When the UAV flew into the destination (constraint (5), it must fly out of the destination (to another destination or to the landing base). Each UAV must return to the landing base (index the landing base is b ∈ W W W) that is modeled by (6).…”

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confidence: 99%

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UAV Mission Planning with SAR Application

Stecz

Gromada

2020

Sensors

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The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Generate the net S S S where vertices belong to the set W W W = F F F ∪ V V V using algorithms 1 and 2 6:…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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UAV Mission Planning with SAR Application

Stecz

Gromada

2020

Sensors

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Truck and drone routing problem with synchronization on arcs

Chen

Wang

et al. 2022

Naval Research Logistics

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Truck-drone technology that enables drones to launch from or land on a moving truck without the need for the truck to stop is the focus of this study. We define the truck and drone routing problem with synchronization on arcs (TDRP-SA). The TDRP-SA is characterized by synchronization on arcs, time windows, classified customers, direct delivery, multiple trucks, and multiple drones carried by each truck. The TDRP-SA involves synchronization on arcs to permit trucks to dispatch and retrieve drones at suitable moving-LRLs (drone launch/retrieval locations) on arcs of truck routes. A moving-LRL is essentially a drone launch/retrieval location with a moving truck, and the drone launch/retrieval operations do not need special parking for the trucks. We develop a mixed integer nonlinear programming model to address the TDRP-SA. We propose a mathematical analysis method to locate moving-LRLs and to estimate the drone's arrival time at the customers. We develop boundary models through introducing linear piecewise functions to locate moving-LRLs. We provide an adaptive large neighborhood search (ALNS) heuristic. Through computational experiments, the effectiveness of the boundary models, the TDRP-SA model and ALNS-based heuristic are evaluated.

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A multi‐vehicle covering tour problem with speed optimization

2021

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In the multi-vehicle covering tour problem with speed optimization, we aim to construct a set of maximal coverage routes for a fleet of vehicles that serve (observe) a set of secondary sites, given a fixed time schedule and coverage requirements. We develop an exact solution approach using a branch-and-price framework with a label-correcting algorithm and a set of innovative dominance rules to solve the resulting pricing problem. We also develop a two-stage heuristic capable of finding effective initial solutions. In addition, we consider practical extensions to the model by incorporating risk thresholds, energy capacities, and time windows. To validate our proposed solution approaches, we perform an extensive set of numerical experiments. Numerical results show the computational advantage of our proposed solution approaches compared with a state-of-the-art commercial solver.

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Unmanned aerial vehicle routing in the presence of threats

Cited by 29 publications

References 66 publications

UAV Mission Planning with SAR Application

UAV Mission Planning with SAR Application

Truck and drone routing problem with synchronization on arcs

A multi‐vehicle covering tour problem with speed optimization

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