To date, self-driving experimental wheelchair technologies have been either inexpensive or robust, but not both. Yet, in order to achieve real-world acceptance, both qualities are fundamentally essential. We present a unique approach to achieve inexpensive and robust autonomous and semi-autonomous assistive navigation for existing fielded wheelchairs, of which there are approximately 5 million units in Canada and United States alone. Our prototype wheelchair platform is capable of localization and mapping, as well as robust obstacle avoidance, using only a commodity RGB-D sensor and wheel odometry. As a specific example of the navigation capabilities, we focus on the single most common navigation problem: the traversal of narrow doorways in arbitrary environments. The software we have developed is generalizable to corridor following, desk docking, and other navigation tasks that are either extremely difficult or impossible for people with upper-body mobility impairments.
The specific problem of doorway traversal, in particular, presents many challenges---no truly robust and cost-effective door detection and navigation system currently exists @cite_21 . Recent approaches utilize visual properties extracted from images, but such methods suffer from difficulties with lighting conditions as well as susceptibility to a variety of artifacts, leading to computationally demanding and unreliable solutions @cite_15 . For example, the three-camera visual approach in @cite_2 relies on edge detection and vanishing point identification to extract trapezoidal (door-like) structures in the environment; similarly, the method in @cite_16 uses two cameras to identify occupied and free space. Neither approach is robust to poor lighting or visual clutter. Further, the planning mechanisms implemented in both cases may be insufficient for tight doors because the chair may not begin in a pose that is well aligned for traversal. In addition, the approach in @cite_16 lacks a definitive mechanism for navigating the common case of a corridor on the other side of the door.
@cite_15 @cite_21 @cite_16 @cite_2
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We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range finder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, finding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance. Abstract— Several studies have shown that both children andadults benefit substantially from access to a means of indepen-dent mobility. While the needs of many individuals with disabil-ities can be satisfied with traditional manual or poweredwheelchairs, a segment of the disabled community finds it diffi-cult or impossible to use wheelchairs independently. To accom-modate this population, researchers have used technologiesoriginally developed for mobile robots to create “smart wheel-chairs.” Smart wheelchairs have been the subject of researchsince the early 1980s and have been developed on four conti-nents. This article presents a summary of the current state of theart and directions for future research. Key words: artificial intelligence, independent mobility, infra-red range finder, laser range finder, machine vision, powerwheelchairs, robotics, sonar, subsumption, voice control. INTRODUCTION Several studies have shown that both children andadults benefit substantially from access to a means ofindependent mobility, including power wheelchairs, man-ual wheelchairs, scooters, a nd walkers [1–2]. Independentmobility increases vocational and educational opportuni-ties, reduces dependence on caregivers and family mem-bers, and promotes feelings of self-reliance. For youngchildren, independent mobility serves as the foundationfor much early learning [1]. Nonambulatory children lackaccess to the wealth of stimuli afforded self-ambulatingchildren. This lack of exploration and control often pro-duces a cycle of deprivation and reduced motivation thatleads to learned helplessness [3].For adults, independent mobility is an importantaspect of self-esteem and plays a pivotal role in “aging inplace.” For example, if older people find it increasinglydifficult to walk or wheel themselves to the commode,they may do so less often or they may drink less fluid toreduce the frequency of urination. If they become unableto walk or wheel themselves to the commode and help isnot routinely available in the home when needed, a moveto a more enabling environment (e.g., assisted living) maybe necessary. Mobility limitati ons are the leading cause offunctional limitations among adults, with an estimatedprevalence of 40 per 1,000 persons age 18 to 44 and 188per 1,000 at age 85 and older [4]. Mobility difficulties arealso strong predictors of activities of daily living (ADL)and instrumental ADL disabi lities because of the need to In this paper we introduce CoPilot, an active driving aid that enables semi-autonomous, cooperative navigation of an electric powered wheelchair (EPW) for automated doorway detection and traversal. The system has been cleanly integrated into a commercially available EPW, and demonstrated with both joystick and head array interfaces. Leveraging the latest in 3D perception systems, we developed both feature and histogram-based approaches to the doorway detection problem. When coupled with a sample-based planner, success rates for automated doorway traversal approaching 100 were achieved. Navigating within an unknown indoor environment using an electric wheelchair is a challenging task, especially if the user suffers from severe disabilities. In order to reduce fatigability and increase autonomy, control architectures have to be designed that would assist users in wheelchair navigation. We present a framework for vision-based autonomous indoor navigation in an electric wheelchair capable of following corridors, and passing through open doorways using a single doorpost. Visual features extracted from cameras on board the wheelchair are used as inputs for image based controllers built-in the wheelchair. It has to be noted that no a-priori information is utilized except for the assumption that the wheelchair moves in a typical indoor environment while the system is coarsely calibrated. The designed control schemes have been implemented onto a robotized wheelchair and experimental results show the robust behaviour of the designed system. We devise a visual servoing approach for indoor wheelchair navigation.The fundamental tasks of corridor following and doorway passing are tackled.Results in simulation and on a robotic platform show the convergence and validity of the control system.This work is proposed as a first step in developing the concept of semi-autonomous assistive systems.