**1. Introduction**

Traveling autonomously from one place to another is an extremely important subject, which has been a dream for years and studied a lot in recent years. The research on the subject generally focuses on the autonomy of passenger cars going into traffic. The first serious results of the studies on this subject came from the race named Darpa Grand Challenge, which was organized by the organization "Defense Advanced Research Projects Agency (DARPA)," which carries out the United States' advanced technology defense projects, in 2004, and then continued in 2005 and 2007, respectively [1]. After DARPA competitions, lots of autonomous studies have been done all over the world such as in [2–4].

Autonomous traveling is possible and a serious need, not only in the way that passenger cars perform in traffic, but also in closed environments where the use of Global Positioning System (GPS) is not possible and where the density of people is high. People may prefer to go somewhere autonomously in a closed environment even if there is no walking disability. Wheelchairs are very suitable vehicles for this kind of personal transportation. Also, for some people with disabilities, wheelchairs are the only option to get from one place to another. Unfortunately, many people with disabilities lack the ability to safely use their wheelchairs. This situation poses serious risks for them, primarily for the people around them and other environmental elements. In line with all these needs, in this paper, design and development of a fully autonomous smart wheelchair is explained.

In literature, single-person autonomous/semi-autonomous vehicle studies are generally carried out on the wheelchair platform. The wheelchair named TetraNauta in [5] was developed at the University of Seville between 1998 and 2004, and it operates in a known map autonomously. The study shown in [6] explains the smart wheelchair project that started in 2004 at the Massachusetts Institute of Technology. In this study, an efficient, socially acceptable autonomous tourfollowing behavior was developed. In another work about autonomous wheelchairs [7], RGB-D camera was used as the main perception sensor, and the map of the environment is constructed from this. Ref. [8] provides information on the development of a robot operating point (ROS)-based autonomous wheelchair that will work indoors. Ref. [9] mentions an autonomous wheelchair that uses only light detection and ranging (LIDAR) as its environmental measurement unit. This study is carried out using the ROS platform. In [10, 11], a semi-autonomous wheelchair was designed where the chair was controlled via head movements. In [12], the aim is to estimate the chair pose from video data by using machine learning methods using artificial neural networks. Another autonomous wheelchair study [12] provides the results about navigation in cluttered environments without explicit object detection and tracking.

All these studies use autonomous/semi-autonomous wheelchairs, which are converted from a conventional wheelchair platform. They provide information about their wheelchairs' autonomy but not deep information about the conversion itself. The conversion process of autonomous systems is explained in some papers for autonomous automobiles [13, 14] but not very detailed for wheelchairs. The dimensions, velocity capabilities, and differential drive architecture make wheelchairs different than standard automobiles. In this paper, we explain how to convert a conventional wheelchair into an autonomous one which is aimed to be used as a testbed for advanced autonomous algorithms. Besides, the results of localizing and mapping algorithms applied on this testbed are illustrated at the end of the work.
