Evolution of fuzzy behaviors for multi-robotic system

Vadakkepat, Prahlad; Xiao, Peng; Quek, Boon Kiat; Lee, Tong Heng

doi:10.1016/j.robot.2006.07.005

Cited by 24 publications

(6 citation statements)

References 10 publications

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“…Applications of our methods to multi-agent game environments such as Robot Soccer [30][31][32] would be interesting research direction.…”

Section: Discussionmentioning

confidence: 99%

Multi-objective behavior coordination based on sensory network for multiple mobile robots

Nojima

2009

2009 IEEE Workshop on Robotic Intelligence in Informationally Structured Space

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This paper deals with multi-objective behavior coordination and sensory networks based on fuzzy control for multiple mobile robots. In multi-objective behavior coordination, weights are assigned to basic behaviors and updated gradually according to the time series of sensory inputs. A robot with our behavior coordination can take a smooth action. The sensory network is the self-update mechanism of a sensory range in order to realize flexible control with a small number of control rules. Both methods have been successfully applied to a single mobile robot. In this paper, we focus on an environment including multiple mobile robots. We examine the effects of the values for updating behavior weights and sensory ranges on the performance of multiple mobile robots. From the experimental results, we show the effectiveness of the multi-objective behavior coordination and sensory networks in the environment including multiple mobile robots.

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“…Applications of our methods to multi-agent game environments such as Robot Soccer [30][31][32] would be interesting research direction.…”

Section: Discussionmentioning

confidence: 99%

Multi-objective behavior coordination based on sensory network for multiple mobile robots

Nojima

2009

2009 IEEE Workshop on Robotic Intelligence in Informationally Structured Space

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“…The enrichment of situational calculus in [54] with other Computational Intelligence tools, may enable this approach to be extended to very unstructured dynamic environments. Distributed control can also be obtained by evolving fuzzy [174] or neuronal [75] controllers that exhibit some emergent behavior. The biological foundations of the idea of robot swarms are reviewed in [166], including a prospective of their application in [142].…”

Section: Controlmentioning

confidence: 99%

On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development

Duro

Graña

Lope

2010

Information Sciences

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“…For instance, a group of robots are evolved to display aggregation and coordinated movements [9][10][11], perform multi-objective tasks [12,13], and learn to cooperate to play competitive games [14][15][16][17], etc. Evolution of unplanned coordination among independent agents in market selection games is also examined [18].…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Cooperative Behaviours in Physically Heterogeneous Multi-Robot Systems

Yang

Liu

et al. 2012

International Journal of Advanced Robotic Systems

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Evolutionary Robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. The existing works focus on single robot systems or physically homogenous multi-robot teams, although physical heterogeneousness is more prevalent in the real world. It is therefore instructive to examine whether cooperative behaviours can be synthesized using artificial evolution for a team of physically heterogeneous robots. This paper makes an important contribution in answering the question of whether robots with distinct capabilities can synthesize their control strategies to accommodate their own capabilities without human intervention. We present an empirical analysis of the collaboration mechanisms and suggest guidelines about how to choose appropriate evolution methods. Simulated experiments with a team of e-puck robots show that evolution can lead to effective controllers for robots with distinct capabilities.

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Evolution of fuzzy behaviors for multi-robotic system

Cited by 24 publications

References 10 publications

Multi-objective behavior coordination based on sensory network for multiple mobile robots

Multi-objective behavior coordination based on sensory network for multiple mobile robots

On the potential contributions of hybrid intelligent approaches to Multicomponent Robotic System development

The Evolution of Cooperative Behaviours in Physically Heterogeneous Multi-Robot Systems

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