Implementation of Space Exploration Algorithm Duly Integrated with ROS: A Practical Framework

Abstract

Exploration is the process of picking target points that contribute the most to a certain gain function in an initially unknown environment. The most popular approach to exploration is frontier-based exploration, in which frontiers are locations on the threshold between open space and undiscovered space. By traveling to a new frontier, the map of the environment is progressively developed, until there are no more new frontiers to detect. For problems involving mobile robot exploration, this study presents meta-heuristic Aquila optimization algorithm with the deterministic method, for comparison and performance testing in mobile robotics. Matlab(R)-Rosintegration tools and real-world tests were carried out in a Gazebo environment utilizing various map scenarios. During the exploration process, the robot updates the sensor visibility frontier line as it travels to unexplored territory. The frontier line's points are taken to be the swarm population, each with its own positions and costs, allowing for the computing of the next way-point (pose) for the robot. The results demonstrated through extensive simulations depicting different environments, duly demonstrate the effectiveness of the proposed method for mobile robotics.