VOL. 10, NO. 23, DECEMBER 2015 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2015 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 17573 EXPLORATION OF UNKNOWN ENVIRONMENT WITH ACKERMAN MOBILE ROBOT USING ROBOT OPERATING SYSTEM (ROS) M.S. Hendriyawan Achmad, Nur Afzan Murtdza, Nor Anis Aneza Lokman, Mohd Razali Daud, Saifudin Razali and Dwi Pebrianti Robotics and Unmanned Research Group, Instrument & Control Engineering Cluster, Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia E-Mail: hendriyawanachmad@gmail.com ABSTRACT In this paper, authors present a series of work in order to explore unknown environment consists of path and obstacles with the Ackerman model of wheeled mobile robot (car-like). Robot operating system (ROS) is used as a basic operation platform to handle the entire of operation, such as sensor interfacing, 2D/3D mapping, and path planning. ROS is an open source framework and huge constructions consist of methods. The Ackerman mobile robot is a car-like robot as commonly sees, and techniques that have been done in this experiment can be applied to the commercial vehicles as a part of autonomous navigation system which is emerge as big issue nowadays. In this work, we had composed robust existing methods to solve the mapping problem with Ackerman mobile robot. It was concluded that the performance of the proposed work is robust for large mapping within unknown construction building. Keywords: mapping, ackerman mobile robot, robot operating system. INTRODUCTION Mapping is an important step when a mobile robot wants to explore the unknown environment [1], and robot should be able to move and find the pathways to avoid obstacles and achieve the goal [2]. Mobile robot navigation technology is now increasingly sophisticated, with technology of 2D/3D mapping [3]. Many studies have been done on the implementation of 2D/3D SLAM (simultaneous localization and mapping) to determine the goal position respect to the current position of the robot in an area that has not been recognized previously [4]. There are many studies discussing about environment mapping by using a robot with non- holonomic differential model, but it's need a major modifications when adapt to the commercially of the shelf (COTS) vehicles environment due to complexity of kinematics design. Car-like robot which is use Ackerman steering become a solution model to synchronize between research and real implementation in term of SLAM base autonomous navigation. ROS is an open source framework which has capability to handle all software layers from low-level up to high-level layers. Because of that, everyone all over the world can build and share ROS stack and package for certain purposes. High demand on ROS stack from researchers was increasing exponentially the number of ROS stack production year to year. Therefore, ROS provide good methods which were can be chosen base on our own desires and purposes. It is also the product of trade offs and prioritizations made during its design cycle [5]. The contribution of this work is a SLAM solution that is able to describe an unknown environment as a map and possible to reduce the risk on rescue missions for instance. We were able to simultaneously localize and build real time map more than 2 km 2 of our robotic laboratory area less than one hour. The purpose of this experiment was not merely to demonstrate implementation of ROS in car-like robot, but to produce a 2D and 3D map to a particular environment for use by other type of robots for path planning in future operations. Our results satisfy the qualification compare with ground truth. This paper is organized as follows. Section 2 describe related researches which have been done to strengthen our work, then Section 3 describe the Ackermann steering, Ackermann pose estimation, structure of ROS, and followed by the steps required to generate a map using hector SLAM in Section 4. At each stage, come up with solution issue is illustrated and analyzed in section 5. Finally, Conclusions are described in the last Section. Figure-5 shows the test bed use in this experiment. Figure-1. Ackerman mobile robot (with Kinect and PTU- Hokuyo LRF). RELATED WORK S. Gariddo et al. have presented an efficient mapping method on unknown environments with mobile