An Auction-Based Multiple Constraints Task Allocation Algorithm for Multi-UAV System

Qiao, Cheng; Yin, Dong; Yang, Jian

doi:10.1109/crc.2016.011

Cited by 15 publications

(13 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment results identified the effectiveness of its application to intelligent robots. Besides, there are many other models such as contract net (CN) [18], decentralized Markov decision process (Dec-MDP) [19], market auction mechanism [20], and dynamic ant colony's labor division (DACLD) [21]. However, for the battlefield dynamic tasks in confrontational environments, these models and algorithms are weak in some points; they did not take the damaging problem of UAVs and heterogeneous UAVs into account when doing task allocation.…”

Section: Related Workmentioning

confidence: 99%

“…The overlapping qualification is decided by the fact that the environment-threatened value e t i is larger than the threshold, and u j 's last task is a backup copy with the condition that backup copy was not overlapped (see lines [8][9][10][11][12][13][14][15]. In order to obtain a load balance for UAVs, the earliest finish time in a nonoverlapping mode would be calculated for comparison, and the algorithm finally acquires an earlier finish time (see lines [16][17][18][19][20][21]. In line 22, if the algorithm has found a proper UAV u and the corresponding backup copy t i B can be scheduled based on t i P 's earliest finish time, the assigning in the overlapping mode would happen.…”

Section: Assigning Algorithm For Primary Copiesmentioning

confidence: 99%

“…Lines 10-17 mean that the task would be assigned in the overlapping mode firstly. If the task fails to overlap with other tasks, the nonoverlapping mode is to be used for assignment (see lines [18][19][20][21][22][23][24][25]. Lines 26-27 mean that if the allocation has been succeeded, it would end the scanning loop.…”

Section: Assigning Algorithm For Backupmentioning

confidence: 99%

See 2 more Smart Citations

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Chen

Bao

Men

et al. 2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Assigning Algorithm For Primary Copiesmentioning

confidence: 99%

See 1 more Smart Citation

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Chen

Bao

Men

et al. 2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…As the working environment becomes increasingly complicated, a single-unmanned platform can no longer satisfy more task demands due to certain limitations, such as insufficient individual abilities and simple execution approaches [1]. In this context, an inevitable tendency of unmanned platform-based task allocation technique research is to investigate how heterogeneous unmanned platforms can be reasonably allocated in a dynamic condition to complete collaborative tasks rapidly and effectively [2].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Task Allocation of Heterogeneous Multi-Unmanned Platform Based on a Hybrid Improved Contract Net Algorithm

Zhao

2021

IEEE Access

View full text Add to dashboard Cite

On the basis of investigating dynamic collaborative task allocation of heterogeneous multiunmanned platform, a dynamic collaborative task allocation model was constructed to settle these problems in this paper. Moreover, the contract net algorithm was improved by means of mental coefficients, the blackboard model and the buffer pool mechanism; and a hybrid method of three modified contract net algorithms was applied to dynamic collaborative task allocation of heterogeneous multi-unmanned platform. As a result, not only is the task allocation efficiency raised, but outcomes of collaborative task allocation become more reasonable. As demonstrated by relevant laboratory findings, the corresponding performance is significantly boosted.

show abstract

“…The first type involved a larger number of robots performing less demanding tasks such as swarm systems. The second involved a moderate number of robots (10-30) (Sugiyama et al 2016;Cheng et al 2016). The third included a low number of robots performing very complex tasks (Khalfi et al 2016;Wang et al 2016;Hongli et al 2016).…”

Section: Systematic Reviewmentioning

confidence: 99%

Solving the task variant allocation problem in distributed robotics

et al. 2018

View full text Add to dashboard Cite

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system's quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively.

show abstract

An Auction-Based Multiple Constraints Task Allocation Algorithm for Multi-UAV System

Cited by 15 publications

References 5 publications

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

Collaborative Task Allocation of Heterogeneous Multi-Unmanned Platform Based on a Hybrid Improved Contract Net Algorithm

Solving the task variant allocation problem in distributed robotics

Contact Info

Product

Resources

About