Intelligent Mobile Robots

Background/Objectives: A new method is devised to build a collection of wheeled robots that are capable of communicating with each other and mapping a maze in a minimum amount of time by dividing the work between two or more robots. Methods/Statistical analysis: The design and implementation of multiple mapping robots are undertaken using Digital Magnetic Compass, Ultrasonic Sensor, and Arduino. The designed robots use a metric, world-centric approach for mapping algorithm. Robots follow the wall while continuously sending its co-ordinates to the base station. The base station or map monitor has PC with NRF module link connected with mobile robots, and the map is plotted on a GUI. The proposed approach is a low-cost robotic application to solve SLAM problem. Findings: The proposed work is an approach for mapping and exploration of mobile robots. Initially, each robot explores the environment by themselves, and later they communicate with other robots by exchanging sensor information. Magnetometer readings help the robot to estimate their relative location. The predictive models are updated by the robots based on their exploration of the environment. Based on the experiments done it is proved that mapping and map merging decisions made using multiple robots provided better performance than using some other technique which uses a single robot for exploration. The robots communicate with each other very often and verify their location to avoid false-acceptance map matches. In case the robots are about to meet at a point known as meeting point, these robots have prior information about their relative locations, and henceforth these robots can combine their data into a map named as shared map. SLAM technique is used to find out the uncertainty in the mapping of individual robots and merging the map provided by each robot. The coordination of robots and estimation of other robot's location is done using the shared map. The robots used their wireless network available with them for exchanging information with each other. In all official runs, all the robots successfully mapped, merged the maps and explored the environment. The maps produced by the robots during each run looked alike which indicates a high level of accuracy of the system. Application/Improvements: This concept can be implemented for exploration on planets or on rescue missions It can be made further into a SWARM system in which the robots has a very high level of intelligence..