Stigmergy, self-organization, and sorting in collective robotics
Artificial Life
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
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
Digital enzymes: agents of reaction inside robotic controllers for the foraging problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Preference-based policy learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A swarm robot methodology for collaborative manipulation of non-identical objects
International Journal of Robotics Research
Towards temporal verification of swarm robotic systems
Robotics and Autonomous Systems
Information Sciences: an International Journal
Synergy in ant foraging strategies: memory and communication alone and in combination
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Understanding the effect of individual parameters on the collective performance of swarm robotic systems in order to design and optimize individual robot behaviors is a significant challenge. In this paper we present a macroscopic probabilistic model of adaptive collective foraging in a swarm of robots, where each robot in the swarm is capable of adjusting its time threshold parameters following the rules described by Liu et al. 2007. The swarm adapts the ratio of foragers to resters (division of labor) in order to maximize the net swarm energy for a given food density. A probabilistic finite state machine (PFSM) and a number of difference equations are developed to describe collective foraging at a macroscopic level. To model adaptation we introduce the new concepts of the sub-PFSM and private/public time thresholds. The model has been validated extensively with simulation trials, and results show that the model achieves very good accuracy in predicting the group performance of the swarm. Finally, a real-coded genetic algorithm is used to explore the parameter spaces and optimize the parameters of the adaptation algorithm. Although this paper presents a macroscopic probabilistic model for adaptive foraging, we argue that the approach could be applied to any adaptive swarm system in which the heterogeneity of the system is coupled with its time parameters.