Development and Testing of a Robotic Wheelchair System for Outdoor Navigation

A robotic wheelchair system must be able to navigate outdoors as well as indoors. This paper describes the outdoor navigation system for our robotic wheelchair, Wheelesley, which uses a vision system to avoid obstacles and to stay centered on the current path. User tests were performed on 7 able-bodied subjects using single switch scanning on an outdoor course. The outdoor navigation system reduced user effort by 74% and reduced the time needed to navigate the test course by 20%. BACKGROUND The goal of this research is to provide people unable to drive a standard powered wheelchair with a mobility device that does not require the assistance of a caregiver. Robotic wheelchairs must be able to navigate in indoor and outdoor environments. A survey of powered and manual wheelchair users found that 56.6% used their wheelchair only outside, 33.3% used their wheelchair both inside and outside, and 10% used their wheelchair only inside [1]. Most prior work on robotic wheelchairs has only addressed the problem of indoor navigation. This research project has developed a robotic wheelchair system that provides navigation assistance in indoor and outdoor environments, allowing its user to drive more easily and efficiently. Acoustic and vision based sensors are used to provide assistance. The wheelchair system is semiautonomous, which takes advantage of the intelligence of the chair’s user by allowing the user to plan the general route while taking over lower level control such as obstacle avoidance and centering on a path. The developed system has an easily customizable user interface that has been tested with eye tracking and with single switch scanning. This paper addresses the development and testing of the outdoor navigation system; for a full report on the wheelchair system, see [2]. RESEARCH QUESTION Can assisted navigation in an outdoor environment improve driving performance when using single switch scanning as an access method? METHODS Sensors used in indoor environments, such as sonar and infrared, are not very effective in outdoor environments. Assistive navigation in an outdoor environment is accomplished using computer vision. We use a STH-V1 Stereo Head from Videre Designs mounted on the front of the wheelchair’s tray to capture images of the world in front of the wheelchair. Disparities of points 1 in the image are used to compute obstacle boundaries for obstacle avoidance. The navigation system also computes the location of the edges of the current path to provide path following. Obstacle avoidance takes priority over path following. The robot will only follow the command of the path following module if there are no obstacles detected. To detect obstacles, the disparity image is scanned horizontally and vertically, looking for changes in disparity between adjacent points that exceed a specified threshold, indicating a likely 1 Disparity measures the difference of the location of a point in the left and the right image of a stereo pair. Disparity is greater for closer objects and smaller for objects in the distance. You can experience this by looking ahead at a scene with some close and some far obstacles. Alternate closing your left and right eyes. Close objects will appear to move more from one image to the next than far objects do.