Hybrid dynamic mobile task allocation and reallocation methodology for distributed multi-robot coordination

Li, Guanghui; Tamura, Yusuke; Wu, Min; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/aim.2012.6265938

Cited by 3 publications

(1 citation statement)

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“…The auction methods have been shown to efficiently produce suboptimal solutions with limited communication range [23]- [26]. In an auction approach, any robot that tries to allocate a task becomes an auctioneer and broadcasts an auction call for the task.…”

Section: Introductionmentioning

confidence: 99%

Ad Hoc Network-Based Task Allocation With Resource-Aware Cost Generation for Multirobot Systems

Lee

Zaheer

Kim

2014

IEEE Trans. Ind. Electron.

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The objective of multirobot task allocation (MRTA) is to assign tasks to robots to minimize the overall cost of task performance. This paper proposes a decentralized MRTA approach considering the robots' residual expendable resources and their limited communication ranges. The proposed approach consists of two algorithms, namely, resource-aware cost generation (RCG) and ad hoc network-based task allocation (ANTA). The RCG algorithm allows each robot to generate a credible cost of task performance considering its residual resources in task planning. The ANTA algorithm constructs the minimal spanning tree network among the robots and determines the robot with the lowest cost for the task through multihop communication in a decentralized manner. The advantages of the proposed approach are minimization of unnecessary task performance cost caused by resource shortage of robots during task execution and the use of an ad hoc network among the robots to allow more robots to participate in the task allocation process. The effectiveness of the proposed approach is demonstrated through computer simulations for multirobot foraging as a test problem.Index Terms-Ad hoc network-based task allocation (ANTA), decentralized task allocation, multirobot systems, resource-aware cost generation (RCG).

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Section: Introductionmentioning

confidence: 99%