On the use of Bayesian Networks to develop behaviours for mobile robots

Lazkano, Elena; Sierra, Basilio; Astigarraga, Aitzol; Martínez-Otzeta, José María

doi:10.1016/j.robot.2006.08.003

Cited by 45 publications

(25 citation statements)

References 23 publications

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“…So, in our model the robot can go through narrow passages successfully. However, in (Lazkano et al, 2007) a reactive navigation method based on Bayesian Networks is proposed. This method only takes into account the door-crossing behaviour.…”

Section: Experimentation Results In Dynamic Environmentsmentioning

confidence: 99%

A Reactive Anticipation for Autonomous Robot Navigation

Ayari¹,

Hadouaj²,

Ghédira³

2012

Serial and Parallel Robot Manipulators - Kinematics, Dynamics, Control and Optimization

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Section: Experimentation Results In Dynamic Environmentsmentioning

confidence: 99%

A Reactive Anticipation for Autonomous Robot Navigation

Ayari¹,

Hadouaj²,

Ghédira³

2012

Serial and Parallel Robot Manipulators - Kinematics, Dynamics, Control and Optimization

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“…A door identification and crossing approach is also presented in (Monasterio et al, 2002), where a neural network based classification method was used for both, the recognition and crossing steps. More recently, in (Lazkano et al, 2007) a Bayesian Network based classifier was used to perform the door crossing task. Doors are assumed to be open, the opening is identified, and doors crossed using sonar sensor information.…”

Section: The Door Identification Taskmentioning

confidence: 99%

Approaches to Door Identification for Robot Navigation

Jauregi¹,

Lazkano²,

Sierra³

2010

Mobile Robots Navigation

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“…Lazkano et al [5] proposed a method for using a Bayesian network as a learning technique to manage task execution in mobile robotics. That research showed that Bayesian networks are valuable learning mechanisms, capable of dealing with the uncertainty and variability inherent in such systems.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network-based behavior control for skilligent robots

Lee

Suh

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-A Skilligent robot must be able to learn skills autonomously to accomplish a task. "Skilligence" is the capacity of the robot to control behaviors reasonably, based on the skills acquired during run-time. Behavior control based on Bayesian networks is used to control reasonable behaviors. To accomplish this, subgoals are first discovered by clustering similar features of state transition tuples, which are composed of current states, actions, and next states. Here, features used in clustering are produced using changes of the states in the state transition tuples. Parameters of Bayesian networks and utility functions are learned separately using state transition tuples belonging to each subgoal. To select the best action while executing a task, the expected utility of each subgoal is calculated by the expected utility function and the robot chooses the action that maximizes expected utility calculated by the maximum expected utility (MEU) function. The MEU function is based on the conditional probabilistic distributions of Bayesian networks and utility functions. We also propose a method for reconstructing learned networks and increasing subgoals by incremental learning.To show the validities of our proposed methods, a task using Dribbling-Box-Into-a-Goal (DBIG) and Obstacle-AvoidanceWhile-Dribbling-Box (OAWDB) skills is simulated and experimented.

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On the use of Bayesian Networks to develop behaviours for mobile robots

Cited by 45 publications

References 23 publications

A Reactive Anticipation for Autonomous Robot Navigation

A Reactive Anticipation for Autonomous Robot Navigation

Approaches to Door Identification for Robot Navigation

Bayesian network-based behavior control for skilligent robots

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