Context-Aware Shared Control of a Robot Mobility Aid for the Elderly Blind

Abstract

This paper describes the use of a Bayesian network to provide context-aware shared control of a robot mobility aid for the frail blind. The robot mobility aid, PAM-AID, is a "smart walker" that aims to assist the frail and elderly blind to walk safely indoors. The Bayesian network combines user input with high-level information derived from the sensors to provide a context-aware estimate of the user's current navigation goals. This context-aware action selection mechanism facilitates the use of a very simple, low bandwidth user interface, which is critical for the elderly user group. The PAM-AID systems have been evaluated through a series of field trails involving over 30 potential users.

Using mixed-initiative human-robot interaction to bound performance in a search task

“…Sometimes mixed-initiative interactions are referred to as adjustable autonomy [1,19], sliding autonomy [2], or shared control [8,13,20]. Of these categories, the solution presented by Crandall and Goodrich, where they created a shared-control algorithm that modified the user's navigational commands based on range sensing, is perhaps the most similar to our current approach in that the user specifies the general direction for the robot to travel, but the robot is given initiative over how the robot will actually move.…”

Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures

“…Bayesian networks are encountered in various applications like filtering junk e-mail (Sahami et al, 1998), assistance for blind people (Lacey & MacNamara, 2000), meteorology (Cano et al, 2004), traffic accident reconstruction (Davis, 2003), image analysis for tactical computer-aided decision (Fennell & Wishner, 1998), market research (Jaronski et al, 2001), user assistance in www.intechopen.com software use , fraud detection (Ezawa & Schuermann, 1995), humanmachine interaction enhancement (Allanach et al, 2004).…”

Evolutionary Methods for Learning Bayesian Network Structures

“…Bayesian networks are encountered in various applications like filtering junk e-mail (Sahami et al, 1998), assistance for blind people (Lacey & MacNamara, 2000), meteorology (Cano et al, 2004), traffic accident reconstruction (Davis, 2003), image analysis for tactical computeraided decision (Fennell & Wishner, 1998), market research (Jaronski et al, 2001), user assistance in sofware use , fraud detection (Ezawa & Schuermann, 1995), human-machine interaction enhancement (Allanach et al, 2004). The growing interest, since the mid-nineties, that has been shown by the industry for Bayesian models is growing particularly through the widespread process of interaction between man and machine to accelerate decisions.…”