Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Task allocation using a distributed market-based planning mechanism
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Wasp-like Agents for Distributed Factory Coordination
Autonomous Agents and Multi-Agent Systems
Swarm-Bot: A New Distributed Robotic Concept
Autonomous Robots
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Division of labor in a group of robots inspired by ants' foraging behavior
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Towards Energy Optimization: Emergent Task Allocation in a Swarm of Foraging Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios
Massively Multi-Agent Technology
Multi-robot task allocation through vacancy chain scheduling
Robotics and Autonomous Systems
Teamwork in self-organized robot colonies
IEEE Transactions on Evolutionary Computation
Optimized stochastic policies for task allocationin swarms of robots
IEEE Transactions on Robotics
Efficient multi-foraging in swarm robotics
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Autonomous Self-Assembly in Swarm-Bots
IEEE Transactions on Robotics
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In this article we present a self-organized method for allocating the individuals of a robot swarm to tasks that are sequentially interdependent. Tasks that are sequentially interdependent are common in natural and artificial systems. The proposed method does neither rely on global knowledge nor centralized components. Moreover, it does not require the robots to communicate. The method is based on the delay experienced by the robots working on one subtask when waiting for input from another subtask. We explore the capabilities of the method in different simulated environments. Additionally, we evaluate the method in a proof-of-concept experiment using real robots. We show that the method allows a swarm to reach a near-optimal allocation in the studied environments, can easily be transferred to a real robot setting, and is adaptive to changes in the properties of the tasks such as their duration. Finally, we show that the ideal setting of the parameters of the method does not depend on the properties of the environment.