Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Proceedings of the fifth international conference on Autonomous agents
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note
RoboCup 2001: Robot Soccer World Cup V
Multi-robot task allocation through vacancy chain scheduling
Robotics and Autonomous Systems
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Repeated auctions for robust task execution by a robot team
Robotics and Autonomous Systems
A game-theoretic approach to non-cooperative target assignment
Robotics and Autonomous Systems
Multi-agent role allocation: issues, approaches, and multiple perspectives
Autonomous Agents and Multi-Agent Systems
Coalition formation for task allocation: theory and algorithms
Autonomous Agents and Multi-Agent Systems
k-means Requires Exponentially Many Iterations Even in the Plane
Discrete & Computational Geometry - Special Issue: 25th Annual Symposium on Computational Geometry; Guest Editor: John Hershberger
Task allocation for robots using inspiration from hormones
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Assessing optimal assignment under uncertainty: An interval-based algorithm
International Journal of Robotics Research
A Generic Framework for Distributed Multirobot Cooperation
Journal of Intelligent and Robotic Systems
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This article presents a subgrouping approach to the multi-robot, dynamic multi-task allocation problem. It utilizes the percentile values of the distributional information of the tasks to reduce the task space into a number of subgroups that are equal to the number of robotic agents. The subgrouping procedure takes place at run-time and at every designated decision-cycle to update the elements of these subgroups using the relocation information of the elements of the task space. Furthermore, it reduces the complexity of the decision-making process proportional to the number of agents via introduction of the virtual representatives for these subgroups. The coordination strategy then uses the votes of the robotic agents for these virtual representatives to allocate the available subgroups. We use the elapsed time, the distance traveled, and the frequency of the decision-cycle as metrics to analyze the performance of this strategy in contrast to the prioritization, the instantaneous, and the time-extended coordination strategies.