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In this paper we focus on the task of tracking multiple moving objects in rapidly changing, dynamic environments. Objects are extracted from laser range finder images and correspondences between successive images are established by network optimization techniques. The approach is implemented on a robotic wheelchair, used in two applications and evaluated experimentally.

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This paper describes the hardware design, control, and navigation system of and some preliminary experiments with the robotic wheelchair Mobility Aid for elderly and disabled people (MAid). MAid's general task is to transport people with severely impaired motion skills. The authors did not set out to reinvent and redevelop the set of standard skills of so-called intelligent wheelchairs, such as FollowWall, FollowCorridor, PassDoorway, which are commonly described in the literature. These maneuvers require motion control skills that disabled people, in spite of their disabilities, are eager to learn and quite good at using. Instead, this work focused on generalizing the approach to fine motion control by considering those maneuvers identified as very burdensome due to their duration and required concentration. One of these functions is deliberative locomotion in rapidly changing, large-scale environments, such as shopping malls, entry halls of theaters, and concourses of airports or railway stations, where tens or hundreds of people and objects move around. MAid's performance was tested in the central station of Ulm during rush hour and in the exhibition halls of the Hannover Messe '98, the largest industrial fair in the world. Altogether, MAid has survived more than 36 h of testing in public, crowded environments with heavy passenger traffic.