Showing papers on "Obstacle avoidance published in 1981"

Rover visual obstacle avoidance

Abstract: The Stanford AI Lab cart is a remotely controlled TV equipped mobile robot. A computer program has driven the cart through cluttered spaces, gaining its knowledge of the world entirely from images broadcast by the onboard TV system. The cart uses several kinds of stereo to locate objects around it in 3D and to deduce its own motion. It plans an obstacle avoiding path to a desired destination on the basis of a model built with this information. The plan changes as the cart perceives new obstacles on its journey. The system is reliable for short runs, but slow. The cart moves one meter every ten to fifteen minutes, in lurches. After rolling a meter it stops, takes some pictures and thinks about them for a long time. Then it plans a new path, executes a little of it. and pauses again. It has successfully driven the cart through several 20 meter courses (each taking about five hours) complex enough to necessitate three or four avoiding swerves. Some weaknesses and possible improvements were suggested by these and other, less successful, runs.

A systematic study of driver steering behaviour.

Abstract: A sequence of driving tasks has been carried out in a driving simulator The initial tests represented lane tracking along a serpentine roadway and were employed to verify the operation of the simulator and the ability of a computer algorithm to fit linear driver models to experimental data A second series of tests involved an obstacle avoidance manoeuvre in both a car and a truck These latter simulator runs were augmented by field trials in an automobile during which driver eye point-of-regard data were recorded Eye point-of-regard results from both simulator and field trials were compared and employed in formulating a simple driver model for the obstacle avoidance manoeuvre The results from a preliminary fitting of this model to the experimental data are reported It was found that a single linear model of the driver's dynamic characteristics can be used to represent adequately all of the driver response data measured in the present study