scholarly journals Modified Consensus Based Auction Algorithm for Task Allocation of Multiple Unmanned Aerial Vehicle

2014 ◽  
Vol 23 (4) ◽  
pp. 197-202
Author(s):  
Min-Geol Kim ◽  
Suk-Hoon Shin ◽  
Eun-Bog Lee ◽  
Sung-Do Chi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Auction Algorithm ◽  
Aerial Vehicle

Distributed task allocation with critical tasks and limited capacity

Robotica ◽  
2021 ◽  
pp. 1-25
Author(s):  
An Zhang ◽  
Mi Yang ◽  
Bi Wenhao ◽  
Fei Gao
Keyword(s):  
Numerical Simulations ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Network Capacity ◽  
Allocation Problem ◽  
Limited Capacity ◽  
Performance Impact ◽  
Task List ◽  
Aerial Vehicle ◽  
Traditional Performance

Abstract This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve

2019 ◽  
Vol 233 (13) ◽  
pp. 4735-4749 ◽  
Author(s):  
Fei Yan ◽  
Xiaoping Zhu ◽  
Zhou Zhou ◽  
Yang Tang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Pythagorean Hodograph ◽  
Aerial Vehicle ◽  
Path Lengths ◽  
Target Path

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.

scholarly journals Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194690 ◽  
Author(s):  
He Luo ◽  
Zhengzheng Liang ◽  
Moning Zhu ◽  
Xiaoxuan Hu ◽  
Guoqiang Wang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Integrated Optimization ◽  
Aerial Vehicle

scholarly journals An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2645 ◽  
Author(s):  
Xiaowei Fu ◽  
Hui Wang ◽  
Bin Li ◽  
Xiaoguang Gao
Keyword(s):  
Active Learning ◽  
Unmanned Aerial Vehicle ◽  
Manifold Learning ◽  
Task Allocation ◽  
Sampling Strategy ◽  
Sparse Graph ◽  
Learning Method ◽  
Training Set ◽  
Aerial Vehicle ◽  
The Impact

This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.

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