Publications

Abstract : Multi-robotic systems of varying sizes and levels of complexity can effortlessly address real-world distributed tasks like exploration, catastrophic surveillance, logistics, and industrial manufacturing. To perform such tasks efficiently, individual robots should communicate and collaborate with each other to cooperatively map the area of interest. The primary advantage of multi-robot systems over a single stand-alone robot is the enhanced spatial coverage, significantly reducing the exploration time and energy required for unknown terrain mapping. However, a large number of robots can increase the overall cost as well as the complexity of the operation by introducing additional redundancy. Therefore, the objective of this research is to study the functionality of multi-robot systems by investigating map-merging and nearest-robot navigation using a case study of Turtlebot3. In this study, computational simulations are performed in the ROS framework where multiple robots are deployed to generate individual local maps which are further transformed to create a global world map. This global map is used to compute the Euclidean distance subsequently enabling the nearest member goal navigation. Indoor experimental tests are performed to verify the simulation study, demonstrating that map merging and localised nearest member navigation can cut down operational time. The overall operational time was reduced by 23.43 % with the deployment of two robots and 45.85 % with three robots in the simulation environment, hence verifying the reduction in mission completion time.