Multi-Objective Route Planning for UAV

Xing, Chen; Xu, Rong; Zhao, Jiabao

doi:10.1109/icisce.2017.215

Cited by 8 publications

(5 citation statements)

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“…However, those proposals (designed for a 2D environment) failed to be applied in 3D operational space, since more constraints need to be modeled to acquire the optimal trajectories. For 3D trajectory planning, algorithms like D * [12], rapidly exploring random tree (RRT) [13], bio-inspired algorithms [14], [15], or an evolutionary algorithm (EA) [16]- [19] are used.…”

Section: A Related Workmentioning

confidence: 99%

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

Ahmad

Msm

et al. 2020

IEEE Access

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The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner based on particle swarm optimization and a proposed surveillance area importance updating mechanism aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones. We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner. Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially visit important areas while obtaining a high fitness value in various practical situations.INDEX TERMS Particle swarm optimization, 3D path planning, surveillance area priority, multiple unmanned aerial vehicles.

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Section: A Related Workmentioning

confidence: 99%

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

Ahmad

Msm

et al. 2020

IEEE Access

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“…To act independently and aware of the rules and regulations for finding the path of UAVs, noncooperative techniques are used. To solve the reconnaissance path for the UAV, the ACO algorithm was proposed in Chen et al 118 For radiation dose mapping, an efficient routing planning technique was proposed in Morita et al 119 The routing algorithm based on the graph was proposed, which helps in avoiding collisions and route planning of UAVs with moving objects 120 . For military engagement, surveillance, and monitoring various spaces, a circular digraph was proposed in Bogdanowicz 121 .…”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

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SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

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“…Chen et al. (2017) developed an ant colony algorithm for multi‐UAV path planning, and a greedy algorithm for task allocation. In scenario, 68 targets are visited via four UAVs with minimizing travel time.…”

Section: Introductionmentioning

confidence: 99%

“…Coelho et al (2017) dealed with heterogeneous UAV fleet routing problem, including limited autonomy by covering multiple charging stations and operational necessities. Chen et al (2017) developed an ant colony algorithm for multi-UAV path planning, and a greedy algorithm for task allocation. In scenario, 68 targets are visited via four UAVs with minimizing travel time.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimization of transporting blood products by routing UAVs: the case of Istanbul

Özkan

2022

Int Trans Operational Res

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Since the blood is necessary for surgical operations, disease treatments, chronic disorders, and traumatic accidents, it is staminal to manage the flow in the supply chain of blood products from donors to patients that can save lives. Therefore, this paper focuses on transporting blood products from distribution centers to hospitals in cities by routing unmanned aerial vehicles (UAVs). The problem includes two objective functions as minimizing the used number of UAVs and their total travel distances, simultaneously by considering range, payload weight, and payload volume of UAVs. The problem also covers the blood product demands of hospitals and the supply capacities of distribution centers. A multi-objective integer programming (MOIP) model and three multi-objective metaheuristics are designed to solve the defined problem. To test the effectiveness of the proposed methods, real-life blood product demands of hospitals in Istanbul for the year 2019 are obtained from the Turkish Red Crescent and several scenarios are created as the case study. In scenarios, two types of vertical take-off and landing UAVs are considered, the MOIP is developed by using ILOG, solved via CPLEX, and the metaheuristics are coded in MATLAB. The results reveal that the proposed methods can find good solutions for the problem in acceptable CPU times.

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Multi-Objective Route Planning for UAV

Cited by 8 publications

References 0 publications

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Multi‐objective optimization of transporting blood products by routing UAVs: the case of Istanbul

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