Judhi Santoso
In robotics there is a problem where a robot will explore a new environment that has never been explored before. For this case, robots must be able to explore their environment and construct folders simultaneously. METHOD METHODS Map In real time along with the process of exploration this environment is generally known as the name Simultaneous Localization and Mapping (Slam). Slam is a computational problem for constructing and updating maps of an unknown environment and with the same time tracking the location of the robot on the map. This study aims to find autonomous exploration methods during slam. The method developed is a combination of the Frontier method and probabilistic method using information theory (information gain and entropy) to determine the exploration route. The benefits of this study can be used in various industries. A mobile robot with autonomous slam capabilities can help in the mission of rescue on disaster sites or accident sites with a dangerous environment where conventional slam processes that require manual control cannot be done because of connectivity problems or other things. The MAP instructed by the robot can provide Medan information faced so that it can help plan in the rescue mission.
Methods or algorithms that allow a mobile robot to explore autonomous during the Slam process.
At present most robots in conducting a Slam process are still controlled by humans. Not often control is done wirelessly and remotely. This raises problems when the terrain does not allow to ensure network stability and communication between the base station and robots. Therefore a method is needed to explore autonomous along with the Slam process.