Multi-Robot Task Allocation Based on Combinatorial Auction

Wen, Xiao; Zhao, Zhengang

doi:10.1109/iccma54375.2021.9646189

Cited by 3 publications

(2 citation statements)

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“…There are many different types of auctions, which are typically differentiated based on the items that are auctioned and the structure of the auction. Tasks may be allocated in single-item auctions (e.g., [42,84,90]), multi-item auctions (e.g., [86,89]) or combinatorial auctions (e.g., [91][92][93]). Sequential single-item auctions are relatively simple and only permit the allocation of one task at a time while multiple tasks may be allocated in parallel in a given round in multi-item auctions.…”

Section: Market-based Approachesmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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Purpose of Review Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized. Recent Findings In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems. Summary We first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

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Section: Market-based Approachesmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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“…Although the auction algorithm above can improve task allocation, it necessitates high real-time communication for the system [19]. Moreover, if there are multiple pending tasks, the order of task releases impacts the overall completion efficiency [20]. For the task-allocation problem among multiple AUVs, the computational complexity escalates as the number of AUVs increases.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Chen,

Zhang,

Wang

et al. 2023

Electronics

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For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

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