Abstract—This paper proposes a solution to a door crossing problem in unknown environments for an autonomous wheelchair. The problem is solved by a dynamic path planning algorithm implementation based on successive frontier points determination. An adaptive trajectory tracking control based on the dynamic model is implemented on the vehicle to direct the wheelchair motion along the path in a smooth movement. An EKF feature-based SLAM is also implemented on the vehicle which gives an estimate of the wheelchair pose inside the environment. The SLAM allows the map reconstruction of the environment for future safe navigation purposes. The entire system is evaluated in a real time simulator of a robotic wheelchair. I. INTRODUCTION he integration of robotics issues into the medical field has become of a great interest in the recent years. Mechanical devices specially developed for surgery like robot manipulators, control algorithms for tele-operation of those robots and cognitive algorithms for user decision learning are some examples of robotic applications in medicine. On the other hand, service, assistance, rehabilitation and surgery are the more benefited human health-care areas by the recent advances in robotics. Precisely, autonomous and safe navigation of wheelchairs inside known and unknown environments is one of the important goals in assistance robotics. A robotic wheelchair can be used to allow people with both lower and upper extremity impairments or severe motor dysfunctions overcome the difficulties in driving a wheelchair. The robotic wheelchair system integrates a sensory subsystem, a navigation and control module and a user-machine interface to guide the wheelchair in autonomous or semi-autonomous mode [20], [21], [22]. In autonomous mode, the robotic wheelchair goes to the chosen destination without any participation of the user in the control. This mode is intended for people who have difficulties to continuously give a control command to guide Manuscript received March 1, 2009. This work was supported in part by the CONICET, Argentina and FAPES-Brazil (Process: 39385183/2007). F. A. C., is with the Institute of Automatics, National University of San Juan, Argentina (e-mail: fauat@inaut.unsj.edu.ar). C. D. is with the Electrical Engineering Department of Unversidade Federal do Espírito Santo, Brazil, Av. Fernando Ferrari 514, Vitória, Espírito Santo, Brazil (corresponding author; e-mail: celsodelacruz@gmail.com). R. C. is with the Institute of Automatics, National University of San Juan, Argentina (e-mail: rcarelli@inaut.unsj.edu.ar). T. B. is with Universidade Federal do Espírito Santo, Brazil (e-mail: teodiano@ele.ufes.br). the wheelchair. In the semi-autonomous mode the user share the control with the robotic wheelchair. In this case only some motor skills are needed from the user. One of the solutions to the autonomy problem of mobile vehicles provided by the robotics field is the implementation of a SLAM (Simultaneous Localization and Mapping) algorithm [1]. SLAM is a recursive probabilistic algorithm that concurrently builds a map of the environment while it localizes the mobile vehicle at the same time, minimizing errors [2]. Although this algorithm is processing time demanding, it becomes a powerful solution when the vehicle has to navigate unsensored or in unknown environments, obtaining a reliable map of it [3]. From its earlier beginning, the SLAM has been implemented in several algorithms [4, 5], being the EKF (Extended Kalman Filter) the most used by the scientific community [6, 7]. The Particle Filter (PF) and the Unscented Kalman Filter (UKF) have proven to be better approaches to the SLAM problem. The Particle Filter solves the gaussianity restriction of the models involved in the SLAM [8] whereas the UKF has shown a better performance dealing with non-linear models of the vehicle and the measurements [8]. Despite of the fact that the map built by the SLAM could be of different types -topological, metric, hybrid [8]- the most used map is a metric feature-based map, which extracts some geometrical constrain from the environment -e.g., corners, lines, color patterns [9]-. These features are then used to localize the vehicle inside that environment. In this paper, a door crossing problem in unknown environments is addressed. It is considered the case of crossing a doorway by an autonomous wheelchair. The vehicle is an unicycle type with two independent motors and a mini-pc onboard. It is also equipped with a range laser sensor to obtain measurements of the environments. The door crossing problem is solved by a dynamic path planning algorithm implementation based on successive frontier points determination. An adaptive trajectory tracking control based on the dynamic model is implemented on the vehicle. These kinds of controls are designed for mobile vehicles that transport heavy loads (the case of a wheelchair) and/or execute high velocity tasks. The controller drives the wheelchair motion along the path in a smooth movement. An EKF feature-based SLAM is also implemented on the vehicle. The SLAM gives an estimate of the wheelchair pose -position and orientation- inside the environment with minimized errors, which is then used by the trajectory controller. The SLAM allows the map reconstruction of the environment for future safe navigation purposes. The features extracted correspond to lines and corners -concave and convex-. Solution to a Door Crossing Problem for an Autonomous Wheelchair Fernando Auat Cheein, Celso De La Cruz, Ricardo Carelli and Teodiano F. Bastos-Filho T The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems October 11-15, 2009 St. Louis, USA 978-1-4244-3804-4/09/$25.00 ©2009 IEEE 4931