Design and Development of an Indigenous Mobile Robot to Navigate in Cluttered Environment

Abstract

The field of smart autonomous systems has experienced significant growth in recent years, with the development of robots aimed at assisting humans in various tasks. In particular, autonomous manipulators have been designed for disaster management and other situations where humans are inaccessible. This master's thesis presents the design and development of a mobile manipulator that can autonomously move in cluttered environments and perform pick and place tasks using 2D SLAM on ROS and 3D camera-based object detection. The proposed solution addresses the SLAM problem by utilizing the gmapping SLAM algorithm, which allows the robot to simultaneously locate itself and map its surroundings. The robot is equipped with a custom-made rover and a 6-DOF robotic arm assembled from ready-made links with joint servos. The arm is used to perform the pick and place tasks, and the 3D camera is used to estimate the coordinates of the targeted object, which is then used to control the robotic arm using inverse kinematics. The localization of the robot is done through 2D pose estimation using Kalman filter, and the destination position is set via RVIZ. The robot is designed to operate in indoor environments and can navigate autonomously using the 2D SLAM technique. The project demonstrates that the robot is capable of detecting the target object's 3D pose, estimating its coordinates, and accurately moving the robotic arm to achieve the desired pick and place task. Real experiments and demonstrations of the mobile manipulator's capabilities were performed using two Arduinos, one controlling the rover's motor and the other controlling the robotic arm's servos. The results of the experiments confirm the robot's ability to move autonomously and perform pick and place tasks accurately and efficiently. Overall, the mobile manipulator designed in this thesis provides a reliable solution for assisting humans in disaster management scenarios and other inaccessible environments. The use of 2D SLAM, 3D camera-based object detection, and inverse kinematics control for the robotic arm ensures efficient and accurate navigation and pick and place operations. The project can be extended to more challenging environments, such as outdoor and unstructured environments, with the integration of advanced sensors and algorithms.