scholarly journals A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 443 ◽  
Author(s):  
Zhe Zhao ◽  
Jian Yang ◽  
Yifeng Niu ◽  
Yu Zhang ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search Algorithm ◽  
Pseudospectral Method ◽  
Task Assignment ◽  
Mission Planning ◽  
Planning Problem ◽  
B Spline ◽  
Aerial Vehicles ◽  
Third Stage ◽  
Multiple Unmanned Aerial Vehicles

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

Multiobjective Mission Planning for UAV under Uncertain Environment

2014 ◽  
Vol 556-562 ◽  
pp. 4435-4438
Author(s):  
Jing Yao Zhu ◽  
Qi Fang He ◽  
Tie Zhu Wang ◽  
Zu Tong Wang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Uncertainty Theory ◽  
Multiobjective Programming ◽  
Flight Time ◽  
Programming Method ◽  
Mission Planning ◽  
Planning Problem ◽  
Uncertain Environment ◽  
Aerial Vehicles ◽  
Combat Environment

The combat environment of Unmanned Aerial Vehicles (UAVs) is filled with uncertain factors, which is complex and dynamic. This paper is devoted to the UAV mission planning problem under uncertain environment with three optimization objectives, such as flight time, fuel usage and threat imposed by enemy. Based on the uncertainty theory and multiobjective programming method, the UAV uncertain multiobjective mission plaaning model is built and solved.

scholarly journals Task Assignment for Multi-UAV under Severe Uncertainty by Using Stochastic Multicriteria Acceptability Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoxuan Hu ◽  
Jing Cheng ◽  
He Luo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Uncertain Information ◽  
Simulation Experiments ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Severe Uncertainty ◽  
Task Assignment Problem ◽  
Multi Uav ◽  
Assignment Approach

This paper considers a task assignment problem for multiple unmanned aerial vehicles (UAVs). The UAVs are set to perform attack tasks on a collection of ground targets in a severe uncertain environment. The UAVs have different attack capabilities and are located at different positions. Each UAV should be assigned an attack task before the mission starts. Due to uncertain information, many criteria values essential to task assignment were random or fuzzy, and the weights of criteria were not precisely known. In this study, a novel task assignment approach based on stochastic Multicriteria acceptability analysis (SMAA) method was proposed to address this problem. The uncertainties in the criteria were analyzed, and a task assignment procedure was designed. The results of simulation experiments show that the proposed approach is useful for finding a satisfactory assignment under severe uncertain circumstances.

Trajectory planning with mid-air collision avoidance for quadrotor unmanned aerial vehicles

2021 ◽  
pp. 095441002110440
Author(s):  
Yuhang Jiang ◽  
Shiqiang Hu ◽  
Christopher J Damaren
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Pseudospectral Method ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Optimal Control Algorithms

Flight collision between unmanned aerial vehicles (UAVs) in mid-air poses a potential risk to flight safety in low-altitude airspace. This article transforms the problem of collision avoidance between quadrotor UAVs into a trajectory-planning problem using optimal control algorithms, therefore achieving both robustness and efficiency. Specifically, the pseudospectral method is introduced to solve the raised optimal control problem, while the generated optimal trajectory is precisely followed by a feedback controller. It is worth noting that the contributions of this article also include the introduction of the normalized relative coordinate, so that UAVs can obtain collision-free trajectories more conveniently in real time. The collision-free trajectories for a classical scenario of collision avoidance between two UAVs are given in the simulation part by both solving the optimal control problem and querying the prior results. The scalability of the proposed method is also verified in the simulation part by solving a collision avoidance problem among multiple UAVs.

Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints

2019 ◽  
Vol 234 (3) ◽  
pp. 760-773 ◽  
Author(s):  
Guangtong Xu ◽  
Teng Long ◽  
Zhu Wang ◽  
Li Liu
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Precedence Constraints ◽  
Task Execution ◽  
Aerial Vehicles ◽  
Multiple Tasks ◽  
Multiple Unmanned Aerial Vehicles ◽  
Multiple Task ◽  
Execution Order

This paper presents a modified genetic algorithm using target-bundle-based encoding and tailored genetic operators to effectively tackle cooperative multiple task assignment problems of heterogeneous unmanned aerial vehicles. In the cooperative multiple task assignment problem, multiple tasks including reconnaissance, attack, and verification have to be sequentially performed on each target (e.g. ground control stations, tanks, etc.) by one or multiple unmanned aerial vehicles. Due to the precedence constraints of different tasks, a singular task-execution order may cause deadlock situations, i.e. one or multiple unmanned aerial vehicles being trapped in infinite waiting loops. To address this problem, a target-bundled genetic algorithm is proposed. As a key element of target-bundled genetic algorithm, target-bundle-based encoding is derived to fix multiple tasks on each target as a target-bundle. And individuals are generated by fixing the task-execution order on each target-bundle subject to task precedence constraints. During the evolution process, bundle-exchange crossover and multi-type mutation operators are customized to generate deadlock-free offspring. Besides, the time coordination method is developed to ensure that task-execution time satisfies task precedence constraints. The comparison results on numerical simulations demonstrate that target-bundled genetic algorithm outperforms particle swarm optimization and random search methods in terms of optimality and efficiency.

Optimal scheduling for aerial recovery of multiple unmanned aerial vehicles using genetic algorithm

2019 ◽  
Vol 233 (14) ◽  
pp. 5347-5359 ◽  
Author(s):  
Yongbei Liu ◽  
Naiming Qi ◽  
Weiran Yao ◽  
Yanfang Liu ◽  
Yuan Li
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Recovery Time ◽  
Search Algorithm ◽  
Random Search ◽  
Time Windows ◽  
Application Range ◽  
Aerial Vehicles ◽  
Path Representation ◽  
Multiple Unmanned Aerial Vehicles

The ability to deploy multiple unmanned aerial vehicles expands their application range, but aerial recovery of unmanned aerial vehicles presents many unique challenges owing to the number of unmanned aerial vehicles and the limited recovery time. In this paper, scheduling the aerial recovery of multiple unmanned aerial vehicles by one mothership is posed as a combinatorial optimization problem. A mathematical model with recovery time windows of the unmanned aerial vehicles is developed to formulate this problem. Furthermore, a genetic algorithm is proposed for finding the optimal recovery sequence. The algorithm adopts the path representation of chromosomes to simplify the encoding process and the genetic operations. It also resolves decoding difficulties by iteration, and thus can efficiently generate a recovery timetable for the unmanned aerial vehicles. Simulation results in stochastic scenarios validate the performance of the proposed algorithm compared with the random search algorithm and the greedy algorithm.

A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

2015 ◽  
Vol 03 (03) ◽  
pp. 205-219 ◽  
Author(s):  
Jingjing Wang ◽  
Y. F. Zhang ◽  
L. Geng ◽  
J. Y. H. Fuh ◽  
S. H. Teo
Keyword(s):  
Task Scheduling ◽  
Search Algorithm ◽  
Time Windows ◽  
Task Assignment ◽  
Flight Path ◽  
Mission Planning ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Good Plan

This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.

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