Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Liu, Jiajie; Wang, Weiping; Li, Xiaobo; Wang, Tao; Bai, Senyang; Wang, Yanfeng

doi:10.2991/ijcis.11.1.81

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…For instance, Refs. [ 37 , 38 , 39 ] present different GA-based planner for scheduling the observation tasks of different satellites, while [ 23 , 40 , 41 , 42 , 43 ] use multiple-objective evolutionary algorithms to solve task planning problems for multiple UAVs engaged in performing monitoring tasks in dissected areas of interest. Although our planner also uses a GA algorithm to determine the best solution plans for a given scenario, it solves a different type of monitoring task mission problem, involving exogenous time-varying events (i.e., weather) and time-dependent mission requirements.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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“…Wojciech Stecz et al discuss the algorithm for determining the exact flight path of the designated reconnaissance purpose by utilizing the task scheduling algorithm with the time window to solve the UAV task allocation problem, and they propose utilizing the MILP formula to solve the UAV flight path planning problem [23]. Jiajie Liu et al [24] propose a task-planning model utilizing a set of topics with related functions as basic task units. The model automatically generates task planning schemes based on the input task priority execution order.…”

Section: A Related Workmentioning

confidence: 99%

Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

et al. 2020

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Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the Rapidly exploring Random Tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.

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“…In [20], the authors developed an improved 324 | P a g e www.ijacsa.thesai.org Multiobjective Particle Swarm Optimization (MOPSO) algorithm to find collision-free and feasible paths with various minimum factors such as altitude, length and angle variable rate. The authors in [21] have improved a Non-dominated Sorting Genetic Algorithm III (NSGA-III) by adding adaptive genetic operators in the offspring population generation to solve the path planning problems. In [22], an improved multiobjective ACO algorithm has been adopted in which the objective function for optimization is formulated to make UAV drone following a short, safe and smooth path.…”

Section: Introductionmentioning

confidence: 99%

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

Jarray¹

2020

IJACSA

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In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles' path planning problem. The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints. The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem. A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem's resolution. To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature. These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem. An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones. Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.

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Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Cited by 14 publications

References 26 publications

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

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