Multi­robot coordination in obstacle cluttered environments

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Χαχάλιος, Μιχαήλ

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The rapidly developing field of robotics presents the intricate challenge of coordinating multi­ robot systems in obstacle­rich environments. This thesis delves into this challenge, introducing a suite of optimized algorithms specifically designed for path planning and navigation in complex spatial settings. These algorithms are meticulously crafted to facilitate efficient movement and effective collision avoidance among groups of robots. Employing advanced computational techniques, the algorithms enable rapid decision­making and adaptability, crucial for dynamic environments. Empirical results from this study demonstrate a marked improvement in the coordination effi­ ciency of multi­robot systems. These enhancements have significant implications for various auto­ mated and robotic applications, potentially revolutionizing the way these systems operate in real­world scenarios. Beyond addressing current challenges in multi­robot coordination, this research lays a ro­ bust groundwork for future explorations in this rapidly evolving domain. The technological backbone of this research is the Robot Operating System (ROS) framework, utilizing Python for algorithmic development. Simulation­based evaluations were conducted in the Gazebo environment using Turtlebot models. Complementing these simulations, physical implement­ations were executed using Amigobot robots, provided by the Laboratory of Automation and Robotics. These implementations were tested on labyrinth­like tracks, designed to mimic complex real­world conditions, thereby validating the practical applicability of the developed algorithms.



Multi-robot coordination, Obstacle-rich environments, Path planning algorithms, Autonomous robotic systems, Collision avoidance, Robot Operating System (ROS), Experimental validation in robotics, TurtleBot3 and Amigobot platforms, Graphical structures in robot coordination, Environmental mapping and representation