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

Duro, Richard J.; Graña, Manuel; Lope, Javier de

doi:10.1016/j.ins.2010.02.005

Cited by 56 publications

(15 citation statements)

References 187 publications

(251 reference statements)

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“…MRS is one of the characteristic applied areas of Artificial Intelligence that has gotten an amazing growth since its inception until today [4,5], and it has developed very significant progress in various fields of application [6], becoming a fundamental tool to produce, work and perform dangerous jobs on earth and beyond.…”

Section: Coordination In Multi-robot Systemsmentioning

confidence: 99%

Coordination Model for Multi-robot Systems Based on Cooperative Behaviors

Quiñonez

Tostado

Sánchez

2013

2013 12th Mexican International Conference on Artificial Intelligence

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In this paper we present a coordination model for multi-robot systems based on cooperative behaviors, where the low level behaviors are obtained through artificial neural networks and evolutionary algorithms to achieve a common goal. For this purpose, we have defined two strategies of self-alignment to carry out the docking process between two robots. Both strategies share the same goal of achieving an optimal alignment between two robots. The main difference between these strategies is that: the strategy 1 consists of having one active and one inactive robot, and in the strategy 2 both robots are active. Experimental results are presented by a robotic device simulator (Player) and a multi-robot simulator 2D (Stage) for each strategy displaying differences between both strategies and benefits in time execution. The paper ends with a critical discussion of experimental results.

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Section: Coordination In Multi-robot Systemsmentioning

confidence: 99%

Coordination Model for Multi-robot Systems Based on Cooperative Behaviors

Quiñonez

Tostado

Sánchez

2013

2013 12th Mexican International Conference on Artificial Intelligence

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“…In general terms, a system composed of a team of robots and a hose is a Linked Multicomponent Robotic System (L-MCRS) [14]. Previous works dealt with the control of a team of mobile robots moving in a 2D surface performing the transportation of a hose [15], and control was achieved by the means of Reinforcement Learning [16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Quasi-stationary state transportation of a hose with quadrotors

Sanz

López-Guede

Graña

2015

Robotics and Autonomous Systems

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“…Multi-Component Robotic Systems (MCRS) [22] are currently the focus of great scientific interest because they are expected to provide solutions to the growing set of applications that require groups of autonomous robots able to adapt dynamically to changing environments and act in a coordinated way to perform tasks that could not be performed by a single robot, or perform them in a more e cient or economical way. Examples of such tasks are cooperative mapping of an environment [2], establishing dynamic communication links, and driving a hose to a goal [23,24].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

Graña

Fernández-Gauna

López-Guede

2011

Paladyn, Journal of Behavioral Robotics

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Reinforcement Learning (RL) as a paradigm aims to develop algorithms that allow to train an agent to optimally achieve a goal with minimal feedback information about the desired behavior, which is not precisely specified. Scalar rewards are returned to the agent as response to its actions endorsing or opposing them. RL algorithms have been successfully applied to robot control design. The extension of the RL paradigm to cope with the design of control systems for Multi-Component Robotic Systems (MCRS) poses new challenges, mainly related to coping with scaling up of complexity due to the exponential state space growth, coordination issues, and the propagation of rewards among agents. In this paper, we identify the main issues which o er opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.

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

Cited by 56 publications

References 187 publications

Coordination Model for Multi-robot Systems Based on Cooperative Behaviors

Coordination Model for Multi-robot Systems Based on Cooperative Behaviors

Quasi-stationary state transportation of a hose with quadrotors

Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

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