Deadlock-Free Consecutive Task Assignment of Multiple Heterogeneous Unmanned Aerial Vehicles

2014 ◽  
Vol 51 (2) ◽  
pp. 596-605 ◽  
Author(s):  
Qibo Deng ◽  
Jianqiao Yu ◽  
Yuesong Mei
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Aerial Vehicles

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.

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.

Tracking Multiple Ground Targets in Urban Environments Using Cooperating Unmanned Aerial Vehicles

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Vitaly Shaferman ◽  
Tal Shima
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Task Assignment ◽  
Monte Carlo Study ◽  
Urban Environments ◽  
Test Bed ◽  
Team Members ◽  
Distributed Approach ◽  
Aerial Vehicles ◽  
Cooperative Tracking

A distributed approach is proposed for planning a cooperative tracking task for a team of heterogeneous unmanned aerial vehicles (UAVs) tracking multiple predictable ground targets in a known urban environment. The solution methodology involves finding visibility regions, from which the UAV can maintain line-of-sight to each target during the scenario, and restricted regions, in which a UAV cannot fly, due to the presence of buildings or other airspace limitations. These regions are then used to pose a combined task assignment and motion planning optimization problem, in which each UAV's cost function is associated with its location relative to the visibility and restricted regions, and the tracking performance of the other UAVs in the team. A distributed co-evolution genetic algorithm (CEGA) is derived for solving the optimization problem. The proposed solution is scalable, robust, and computationally parsimonious. The algorithm is centralized, implementing a distributed computation approach; thus, global information is used and the computational workload is divided between the team members. This enables the execution of the algorithm in relatively large teams of UAVs servicing a large number of targets. The viability of the algorithm is demonstrated in a Monte Carlo study, using a high fidelity simulation test-bed incorporating a visual database of an actual city.

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