Study on the real-time task assignment of multi-UCAV in uncertain environments based on genetic algorithm

Chen, Xia; Tang, Ting; Li, Lin

doi:10.1109/ramech.2011.6070459

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…(1-9) in Subsection 3.2 performs better than the fitness function described in eq. (5)(6) in Section III(B) [19]. In both scenarios (terrain with and without obstacles), the overall domain searching time is effectively reduced when domain exploration work is adjacent compared to the non-adjacent work allocation for a given number of drones due to the reduced inter-domain transit time.…”

Section: Discussionmentioning

confidence: 99%

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Partitioning Known Environments for Multi-Robot Task Allocation Using Genetic Algorithms

Mazumder¹,

Phillips²

2020

IJC

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A common challenge facing emergency services, particularly in response to fires and/or earthquakes, is the location and subsequent extraction of people from hazardous buildings in a timely manner. This usually requires emergency service workers to enter the building and put their own lives at risk, even when there may be no people to extract. However, given recent advances in autonomous robotics, drones are expected to help humans in tasks such as search and rescue, and similar tasks, where coverage and time are key parameters. The aim is to complete a comprehensive search of the environment as quickly as feasible. Using multiple drones rather than a single drone can reduce search time, although performance can be poor if the searching is non-coordinated. Therefore, partitioning a terrain is important in order to effectively distribute the drone search work so that good coverage can be achieved in a reasonable amount of time and redundant searching is eliminated. In this paper a novel Square Based Terrain Partitioning (SBTP) algorithm is presented using a genetic algorithm to partition a known environment into multiple domains in a multi-robot exploration system. In addition, a second genetic algorithm is presented to allocate the domain search workload such that, given a certain number of drones, the overall search time is minimized.

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Section: Discussionmentioning

confidence: 99%

“…A hierarchical architecture was applied to each robot. An alternative method based on a Genetic Algorithm (GA) was used for real-time task assignment of Multi-Unmanned Combat Aerial Vehicle in uncertain environments [5]. Irfan Younas et al [6] also proposed a method based on GA for task assignment problems.…”

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confidence: 99%

Partitioning Known Environments for Multi-Robot Task Allocation Using Genetic Algorithms

Mazumder¹,

Phillips²

2020

IJC

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“…A lot of task allocation problem models [ 5 , 9 ] and task assignment solving algorithms [ 10 , 11 , 12 , 13 , 14 , 15 ] have been developed to meet the respective needs of various situations. Some intelligent methods have been proposed for multi-UAV task allocation problem under uncertain situation [ 7 , 8 , 16 , 17 , 18 , 19 ]. References [ 16 , 17 , 18 ] used the concepts of interval uncertainty to model the uncertain factors of task allocation problem and the traditional auction algorithm, genetic algorithm and particle swarm optimization (PSO) are separately used to solve multi-UAV task allocation problems under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Some intelligent methods have been proposed for multi-UAV task allocation problem under uncertain situation [ 7 , 8 , 16 , 17 , 18 , 19 ]. References [ 16 , 17 , 18 ] used the concepts of interval uncertainty to model the uncertain factors of task allocation problem and the traditional auction algorithm, genetic algorithm and particle swarm optimization (PSO) are separately used to solve multi-UAV task allocation problems under uncertainty. Ponda [ 8 ] proposed that the uncertainties in the true environment can be captured as parametric uncertainties in the underlying system models, which can affect various portions of the planning model.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Li³

et al. 2018

Sensors

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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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Task assignment of multi-robot systems based on improved genetic algorithms

Zuo

2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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Study on the real-time task assignment of multi-UCAV in uncertain environments based on genetic algorithm

Cited by 5 publications

References 7 publications

Partitioning Known Environments for Multi-Robot Task Allocation Using Genetic Algorithms

Partitioning Known Environments for Multi-Robot Task Allocation Using Genetic Algorithms

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

Task assignment of multi-robot systems based on improved genetic algorithms

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