An Extension of the Consensus-Based Bundle Algorithm for Multi-agent Tasks with Task Based Requirements

Hunt, Simon; Meng, Qinggang; Hinde, Chris J.

doi:10.1109/icmla.2012.163

Cited by 18 publications

(5 citation statements)

References 8 publications

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“…It has been proven that this method can offer similar solutions to some centralized sequential greedy algorithms and 50% optimality is guaranteed [25]. In recent years, several modifications regarding CBBA have been proposed to extend its functions and application areas, such as heterogeneity of UAVs [26,27], communication constraints [28,29], dynamic task planning [30,29], complex cooperation constraints, [31,32] and others [33,34,35]. However, CBBA is based on the auction principles where generally each UAV is selfish in reducing the local cost generated by it.…”

Section: Related Workmentioning

confidence: 99%

Distributed task allocation with critical tasks and limited capacity

Zhang

et al. 2021

Robotica

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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.

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Section: Related Workmentioning

confidence: 99%

Distributed task allocation with critical tasks and limited capacity

Zhang

et al. 2021

Robotica

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“…For an analysis of CBBA's scalability, see [39]. Of the various extensions and modifications, [40] and [41] address MR task assignments and heterogeneous networks for the ST-MR-TA problem in which multiple robots may be required to service one task [13]. Choi et al [40] addressed the case in which a task requires only one single agent, one or two agents, and exactly two agents of different type.…”

Section: Cbba Extensions and Variationsmentioning

confidence: 99%

“…Choi et al [40] addressed the case in which a task requires only one single agent, one or two agents, and exactly two agents of different type. Hunt et al [41] proposed the consensus-based grouping algorithm that addresses the problem of multiagent multitask assignment with group and equipment-based dependencies, and which can accommodate any number of robots.…”

Section: Cbba Extensions and Variationsmentioning

confidence: 99%

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

Turner

Meng

Schaefer

et al. 2018

IEEE Trans. Cybern.

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This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

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“…Aiming at the above problems, this paper first establishes a multi-UAV, multi-objective, unbalanced, cooperative task assignment model that comprehensively considers various constraints [21]. An improved consensus-based bundle algorithm (CBBA) is used to solve the model [22,23]. Secondly, according to the dynamics of new task occurrence, a dynamic assignment mechanism based on task continuity adjustment and time-window matching is proposed [24,25] to achieve multi-UAV cooperative dynamic task assignment.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Wang

et al. 2022

Aerospace

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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

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An Extension of the Consensus-Based Bundle Algorithm for Multi-agent Tasks with Task Based Requirements

Cited by 18 publications

References 8 publications

Distributed task allocation with critical tasks and limited capacity

Distributed task allocation with critical tasks and limited capacity

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

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