Multirobot coordination in obstacle cluttered environments
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Date
2024-03-15
Authors
Χαχάλιος, Μιχαήλ
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Abstract
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.
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Keywords
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