The k -Unanimity Rule for Self-Organized Decision-Making in Swarms of Robots.

In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action. The novelty of our method is that it allows robots to collectively find consensus on the fastest action without measuring explicitly the execution times of all available actions. We study two analytical models of the decision-making method in order to understand the dynamics of the consensus formation process. Moreover, we verify the applicability of the method in a real swarm robotics scenario. To this end, we conduct three sets of experiments that show that a robotic swarm can collectively select the shortest of two paths. Finally, we use a Monte Carlo simulation model to study and predict the influence of different parameters on the method.

Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer.

We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario.

Dynamics of majority rule with differential latencies.

We investigate the dynamics of the majority-rule opinion formation model when voters experience differential latencies. With this extension, voters that just adopted an opinion go into a latent state during which they are excluded from the opinion formation process. The duration of the latent state depends on the opinion adopted by the voter. This leads to a bias toward consensus on the opinion that is associated with the shorter latency. We determine the exit probability and time to consensus for systems of N voters. Additionally, we derive an asymptotic characterization of the time to consensus by means of a continuum model.

Stability and performance of ant queue inspired task partitioning methods.

In this paper, we consider computing systems that have autonomous helper components which fulfill support functions and that possess reconfigurable hardware so that they can specialize to different types of service tasks. Several self-organized task partitioning methods are proposed that can be used by the helper components to decide how to reconfigure and which service tasks to execute. The proposed task partitioning methods are inspired by the so-called ant queue system that can be found in real ants for partitioning tasks between the individuals. The aim of this study is to investigate basic properties of the task partitioning methods, like stability and efficiency, in order to obtain basic insights into the design of task partitioning methods in self-organized service systems. More precisely, the investigations are threefold: (1) discrete event simulations are used to investigate systems, (2) for a simple version of the task partitioning system analytical stability results are obtained by means of delay differential equation systems and (3) by numerically solving initial value problems.