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.
A wide variety of assistive wheelchair navigation systems have been developed over the past 30 years, and we provide only a brief survey of related literature. Low-cost solutions are generally either limited and semi-autonomous @cite_27 @cite_7 , require an external localization system @cite_8 @cite_10 , are able to operate over short distances only without global localization @cite_29 , or lack global planning capabilities @cite_28 . Fully autonomous navigation approaches typically rely on expensive laser scanner (lidar) sensors @cite_13 @cite_20 @cite_30 @cite_19 @cite_24 (which do have the advantage of being able to operate outdoors @cite_0 @cite_9 @cite_22 ). The goal of existing systems has usually been to provide a working solution in the target environment, without attempting to minimize cost.
@cite_13 @cite_30 @cite_22 @cite_7 @cite_8 @cite_28 @cite_29 @cite_9 @cite_24 @cite_19 @cite_27 @cite_0 @cite_10 @cite_20
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In this paper, we explore the use of synthesized landmark maps for absolute localization of a smart wheelchair system outdoors. In this paradigm, three-dimensional map data are acquired by an automobile equipped with high precision inertial GPS systems, in conjunction with light detection and ranging (LIDAR) systems, whose range measurements are subsequently registered to a global coordinate frame. The resulting map data are then synthesized a priori to identify robust, salient features for use as landmarks in localization. By leveraging such maps with landmark meta-data, robots possessing far lower cost sensor suites gain many of the benefits obtained from the higher fidelity sensors, but without the cost.We show that by using such a map-based localization approach, a smart wheelchair system outfitted only with a 2-D LIDAR and encoders was able to maintain accurate, global pose estimates outdoors over almost 1 km paths. In the present work, a robotic wheelchair navigation system which is specially designed for confined spaces is proposed. In confined spaces, the movements of wheelchairs are restricted by the environment more than other unicycle type vehicles. For example, if the wheelchair is too close to a wall, it can not rotate freely because the front or back may collide with the wall. The navigation system is composed by a path planning module and a control module; both use the environment and robot information provided by a SLAM algorithm to attain their objectives. The planning strategy uses the Monte Carlo technique to find a minimum path within the confined environment and takes into account the variance propagation in the predicted path for ensuring the safe driving of the robot. The objective of the navigation system is to drive the robotic wheelchair within the confined environment in order to reach a desired orientation or posture. In this paper, a robotic wheelchair using only a pan-tilt-zoom camera is proposed for developing a robotic system which enables a user to navigate in complex environment at low cost. For realizing the proposed system, a high accuracy visual marker, LentiBar which is capable of achieving a high level of precision in positioning and orienting a robotic wheelchair is utilized and its application to door crossing for a robotic wheelchair is presented. Since the visual marker is attached on the top of the target door through which the wheelchair is going to pass, the typical problems for achieving geometric features of the environment can be easily addressed. Door crossing navigation methods are also explained, which enable automatic and hybrid navigation of the robotic wheelchair. Finally, experimental results show the feasibility of the proposed navigation method and the robotic wheelchair system. In this paper, we present a system level approach to smart wheelchair system (SWS) navigation in urban environments. The proposed SWS ecosystem has two primary components: a mapping service which generates large-scale landmark maps, and the SWS vehicle itself, which is a client of the mapping service. The SWS prototype integrates 3D LIDAR imaging systems which provide robust perception in unstructured, outdoor environments. It also leverages these same sensors for map-based localization. In demonstrating the efficacy of the approach, the SWS navigated autonomously over a distance of more than 12 km in a representative urban environment without once losing localization, and without the use of GPS.