Environmental Engineering Reference
In-Depth Information
Fig. 7. Example of the navigation strategy. The system use the floor's grid to determine the
best path to reach the hit-point. The destination cell is draw in white, and the cells are
classified as occupied (red), unreachable (orange) and reachable (green).
fromwhich the target is visible. We compute these cells in a pre-process, casting rays from the
surfaces cells to virtual camera positions centered at the grid floor cells. Our scenario model
stores the cells associated to each surface cell. Then, when the system wants to determine
the best destination for a target, it selects the closest cell that belongs to the set of the target's
surface cell. Taking into account that the objects during the task can change their positions, it
is possible that all the cells are occupied, and then the avatar cannot reach the target. In those
cases, the system's logic is the responsible of asking the user to move some objects to be able
to reach the target.
Once the system has the current position of the avatar and the destination position, it
computes the path that allows the avatar to move inside the VE without colliding with any
object. The method used is an implementation of the A* path-finding method that minimizes
the Euclidean distance, and uses the floor's grid to compute a discrete path. After this process,
the system computes a Bezier path that follows the discrete path computed before. This new
path allows the system to perform softer movements and keep a constant speed.
5. Results and discussion
In order to test the suitability of the proposed technological assistance strategies, we have
created a set of specific tasks in a virtual scenario representing a kitchen. We have asked
30 volunteers without cognitive impairments to realize these tasks. We have recorded their
results and asked them to fill a questionnaire about different aspects of these assistance
strategies. The profile of the users can be categorized according to three different criteria:
