Autonomous SLAM based Forklift Robot

This paper proposes and develops a sophisticated design of Autonomous Simultaneous Localization and Mapping (SLAM) based Forklift Robot. The main idea of this work is to design such an object lifting forklift robot which works in dynamic and unknown environment like warehouses to lift and shift the boxes from one place to another. Since the forklift robot is a SLAM base so it has the ability to empower the controller of the robot to take decisions related to the movement of the robot on its own without any human intervention and reach to the objects for detection and lifting. For object detection uses the machine learning approach called Optical Character Recognition (OCR) in which we use K-nearest principle to detect the user define object (box). Our design follows three basic steps: the forward motion planning which is the part of SLAM approach for navigation in forward direction and also generating a real time map. The second step is object detection in which robot match the alphanumeric code written on the box with the code defined by the user to detect and lift that box. The third step is reverse motion planning which follows the map was generated during the forward motion planning to get back its initial position with the box. This map is also display on the user’s interface wirelessly.