Mission Planning for Multiple UAVs in a Wind Field with Flight Time Constraints

Pinto, Virgínia Bentes; Galvão, Roberto Kawakami Harrop; Rodrigues, Leonardo Ramos; Gomes, João P. P.

doi:10.1007/s40313-020-00609-5

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…The battery capacity, flight range, and payload capacity are crucial parameters for drone employment in package delivery. Several approaches have been proposed to overcome the problems, including truck-drone collaboration [15], independent drone delivery [16], and drone delivery with charging stations that enable drones to recharge their batteries [17]. In truck-drone collaboration, the trucks carry parcels and several drones, and the drones mainly concentrate on last-mile delivery.…”

Section: Infrared Technology Guides the Drones To Dedicated Landingmentioning

confidence: 99%

“…Simulation results show that the proposed scheme is capable of selecting the optimal route, which minimizes the total flight length. Pinto et al [16] proposed mission planning for multi-UAV drone delivery under windy conditions. They implemented an MILP to optimize the delivery, with a focus on minimizing the drone flight time during delivery to multiple customers in a single mission.…”

Section: Literature Reviewmentioning

confidence: 99%

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JRCS: Joint Routing and Charging Strategy for Logistics Drones

Arafat

Moh

2022

IEEE Internet Things J.

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Unmanned aerial vehicles (UAVs), commonly known as drones, are currently being used to combat the COVID-19 pandemic through applications, including the delivery of medical supplies, aerial spraying, and public space monitoring. In a pandemic, drone-based delivery is a promising and highly efficient method to reduce transportation time, cost, and exposure to infection. However, owing to both the limited battery lifetime and the limited functions of UAV in-flight missions, it is difficult to implement multiple deliveries over long distances in a single transportation mission. In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization methods are required to design the delivery area networks of customers, charging stations, and delivery routes. We propose a joint routing and charging strategy (JRCS) comprising three phases to perform multiple deliveries in a single mission. We first split the customers of a delivery area using a clustering algorithm according to their distance from the nearest charging station and the maximum flight range. The second phase provides flight segmentation and intersegment routes between the depot, customer locations, and charging stations based on the maximum flight range and safe flight distance. The joint consideration of drone routes with charging stations minimizes the number of charging stations and ensures safe delivery. Finally, we formulate mixed-integer linear programming to solve the drone delivery route problem. According to simulation results, the proposed JRCS outperforms existing delivery approaches in terms of various performance metrics.

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Section: Infrared Technology Guides the Drones To Dedicated Landingmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

JRCS: Joint Routing and Charging Strategy for Logistics Drones

Arafat

Moh

2022

IEEE Internet Things J.

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“…Since expectations preserve convexity, the expected inequality constraints ( 30) -( 33) are still convex. In addition, for simplicity, we choose the local point with v (l) (30). Similarly, we substitute v[n] = v e [n]−E(v w ) into the objective function of (P3.2), which, by Jensen's inequality, provides a lower bound for the mean of cost function in problem (P3.1).…”

Section: Offline Design Based On Spmentioning

confidence: 99%

“…In order to prolong the mission duration, the authors in [28] investigate resonant beam charging-powered UAV-assisted sensing data collection, and jointly optimize the UAV's trajectory and the power of the charging station to maximize the power transmission efficiency, whereas the wind effect and the UAV's energy consumption are not explicitly considered. Considering wind effect, the author in [29] formulates the problem of finding minimum-energy flight paths of the UAV by utilizing or avoiding the wind, while the authors in [30] propose a model to solve a mission planning problem in which UAVs must visit some waypoints in the presence of a wind field to maximize the total reward collected and minimize total flight time. However, the data collection scenario is not explicitly considered in [29] and [30], and the UAV-to-buoys communication is not jointly optimized.…”

Section: Introductionmentioning

confidence: 99%

“…Considering wind effect, the author in [29] formulates the problem of finding minimum-energy flight paths of the UAV by utilizing or avoiding the wind, while the authors in [30] propose a model to solve a mission planning problem in which UAVs must visit some waypoints in the presence of a wind field to maximize the total reward collected and minimize total flight time. However, the data collection scenario is not explicitly considered in [29] and [30], and the UAV-to-buoys communication is not jointly optimized. Finally, to deal with the random line-of-sight (LoS)/non-LoS channel conditions in city environment, the authors in [31] propose a hybrid offline-online design of the UAV's trajectory and communication scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Cyclical Trajectory Design for UAV-Aided Maritime Data Collection in Wind

Zhang¹,

Lyu²,

Fu³

2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV's energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV's flight trajectory subject to wind effect. The conventional successive convex approximation (SCA) method can provide efficient sub-optimal solutions for collecting small/moderate data volume, whereas the solution heavily relies on trajectory initialization and has not explicitly considered wind effect, while the computational/trajectory complexity both become prohibitive for the task with large data volume. To this end, we propose a new cyclical trajectory design framework with tailored initialization algorithm that can handle arbitrary data volume efficiently, as well as a hybrid offline-online (HO 2 ) design that leverages convex stochastic programming (CSP) offline based on wind statistics, and refines the solution by adapting online to real-time wind velocity. Numerical results show that our optimized trajectory can better adapt to various setups with different target data volume and buoys' topology as well as various wind speed/direction/variance compared with benchmark schemes. In particular, our proposed HO 2 design can effectively adapt to random wind variations with feasible and robust online operation, and proactively exploit the wind for further energy savings in both single-buoy and multi-buoy scenarios.

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