Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot: A Control Theory with Genetic Programming Approach

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@Article{Clemente:2018:JIRS,

• author = "Eddie Clemente and Marlen Meza-Sanchez and Eusebio Bugarin and Ana Yaveni Aguilar-Bustos",
• title = "Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot: A Control Theory with Genetic Programming Approach",
• journal = "Journal of Intelligent and Robotic Systems",
• year = "2018",
• volume = "92",
• number = "2",
• pages = "359--380",
• month = oct,
• keywords = "genetic algorithms, genetic programming, evolutionary robotics, behaviours, collision avoidance, autonomous navigation, wheeled mobile robots, velocity constraint, lyapunov stability",
• ISSN = "0921-0296",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/jirs/jirs92.html#ClementeMBA18",
• DOI = "doi:10.1007/s10846-017-0751-y",
• size = "22 pages",
• abstract = "Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviours to relate each control objective with a desired behaviour for the mobile robot. Natural behaviours lead the system to fulfil a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behaviour. The adaptive behaviour, learnt through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behaviour of the mobile robot is the addition of the natural and the adaptive behaviours. Our proposed methodology achieves the discovery of 9402 behaviours without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.",
• notes = "Also known as \cite{journals/jirs/ClementeMBA18}",

}

Genetic Programming entries for Eddie Helbert Clemente Torres Marlen Meza-Sanchez Eusebio Bugarin Carlos Ana Yaveni Aguilar-Bustos

Citations