A Robust, Distributed Task Allocation Algorithm for Time-Critical, Multi Agent Systems Operating in Uncertain Environments

Whitbrook, Amanda; Meng, Qinggang; Chung, Pei-Jung

doi:10.1007/978-3-319-60045-1_8

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…This paper thus represents an advance in the state of the art in time-critical ST-SR-TA multiagent task planning. The integration of the rescheduling and the search exploration modules with the robust version of PI [39] to create a "Super-PI" is left as a subject for future work.…”

Section: Discussionmentioning

confidence: 99%

“…All runs were executed in MATLAB R2013a on the same 64-b Note that tests are carried out in simulation only as trials using PI under uncertain conditions have shown that a robust version of PI is necessary if the algorithm is to perform well in a real environment. Future work will thus aim to integrate the rescheduling and soft-max modules with a robust version of PI that has been developed [39] so that they can be tested in the real world. Table VII summarizes metadata for experiment set A using row communication, and Tables VIII and IX repeat these statistics for mesh and hybrid communication.…”

Section: Experimental Methodologymentioning

confidence: 99%

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Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

Whitbrook

Meng

Chung

2018

IEEE Trans. Automat. Sci. Eng.

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This paper addresses two main problems with many heuristic task allocation approaches-solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensusbased bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm's exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners-This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

Whitbrook

Meng

Chung

2018

IEEE Trans. Automat. Sci. Eng.

Self Cite

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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%

“…Robust planning algorithms were developed to select the best task assignment in order to minimize the effect of uncertainties on the final task score. For parametric uncertainties, Ponda [ 8 ] and Whitbrook [ 19 ] used robust strategies to capture the propagation law of uncertainties in the score function and calculates the expected reward scores to participate in the task allocation process.…”

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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A Review of Task Allocation Methods for UAVs

Skaltsis,

Shin,

Tsourdos

2023

J Intell Robot Syst

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Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches.

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A Robust, Distributed Task Allocation Algorithm for Time-Critical, Multi Agent Systems Operating in Uncertain Environments

Cited by 7 publications

References 29 publications

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

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

A Review of Task Allocation Methods for UAVs

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