We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given cri...
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We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be NP-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks;(b) Unmanned Aerial Vehicles are the robots;and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve confiicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robottaskallocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans;(ii) traveled distances;and (iii) exchanged messages.
This paper proposes a consistency bundle algorithm based on clustering grouping for large-scale taskallocationproblems. Large scale taskallocationproblems often result in incomplete network coverage, exponential i...
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ISBN:
(纸本)9798350390780;9798350379228
This paper proposes a consistency bundle algorithm based on clustering grouping for large-scale taskallocationproblems. Large scale taskallocationproblems often result in incomplete network coverage, exponential increase in communication frequency, and communication obstruction. Therefore, this article first uses clustering algorithms to group robots based on the number of tasks, transforming large-scale problems into small-scale problems;Secondly, use the consistency bundle algorithm to solve the taskallocationproblem for each group separately;Finally, the algorithm was used for simulation experiments on large-scale taskallocationproblems, and the results showed that the proposed algorithm can solve the problem while effectively reducing the number of communications.
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