In two experiments, subjects traveled through virtual mazes, encountering target objects along the way. Their task was to indicate the direction to these target objects from a terminal location in the maze (from which the objects could no longer be seen). Subjects controlled their motion through the mazes using three locomotion modes. In the Walk mode, subjects walked normally in the experimental room. For each subject, body position and heading were tracked, and the tracking information was used to continuously update the visual imagery presented to the subjects on a head-mounted display. This process created the impression of immersion in the experimental maze. In the Visual Turn mode subjects moved through the environment using a joystick to control their turning. The only sensory information subjects received about rotation and translation was that provided by the computer-generated imagery. The Real Turn mode was midway between the other two modes, in that subjects physically turned in place to steer while translating in the virtual maze; thus translation through the maze was signaled only by the computer-generated imagery,

this memory is implemented by a flag e b for each base, which indicates that this base needs to be examined later. This flag is the state of a competition dynamics similar to (3.9): ø e e b = fi b e b \Gamma jfi b je

. One of the fundamental abilities required in autonomous mobile agents is the one of homing. Natural agents like ants solve this problem by mainly using dead-reckoning mechanisms within an egocentric frame of reference. Here we present a biologically inspired orientation mechanism, an adaptive light compass, that was used for homing in "Myrmix ", a mobile robot equipped with infrared and ambient light sensors. The control architecture is adaptive by using a self-organizing neural network. Herewith, the robot learns to associate signals coming from the light sensors with the corresponding motor actions. This approach is less computational than others, since apart from the length of the path travelled it is based on local rules. Preliminary results of experiments with this control architecture are reported and contrasted with a similar, but more computational, architecture introduced by [5]. 1 Introduction Many animals, such as insects use information from the sky to navigate. The kind...

ABSTRACT: We describe some of the results of our program of basic and applied research on navigating without vision. One basic research topic that we have studied extensively is path integration, a form of navigation in which perceived self-motion is integrated over time to obtain an estimate of current posilion and orientation. In experiments on pathway completion, one test of path integration ability, we have found that subjects who are passively guided over the outbound path without vision exhibit significant errors when attempting to return to the origin but are nevertheless sensitive to turns and segment lengths in the stimulus path. We have also found no major differences in path inlegration ability among blirid and sighted populations. A model we havc developed that attributes errors in path integration to errors in encoding the stimulus path is a good beginning toward understanding path integration performance. In otber research on path integration, in which optic flow information was manipulated in addition to the proprioceptive and vestibular information of nonvisual locomotion, we havc found that optic flow is a weak input to the path integration process. In other basic research, our studies of auditory distance perception in outdoor environments show systematic underestimation oC sound source distance. Our applied research has been concerned with developing and evaluating a navigation system for the visually impaired that uses three recent technologies: the Global Positioning System, Geographic Information Systems, and virtual acouslics. Our work shows that there is considerable promise of these three technologies in allowing visually impaired individuals to navigate and learn about unfamiliar environments without the assistance of human guides. (Optoni Vis Sci 2001;78:282-289)

This paper tests the generality and implications of an "encoding-error" model (Fujita et al. 1993) of humans' ability to keep track of their position in space in the absence of visual cues (i.e., by nonvisual path integration). The model proposes that when people undergo nonvisually guided travel, they encode the distances and turns that they experience, and their errors reflect systematic inaccuracies in the encoding process. Thus when people try to return to the origin of travel, they base their response on mis-encoded values of the outbound distances and turns. The two experiments reported here addressed three issues related to the model: (i) whether path integration is context-dependent and if so, how rapidly it adapts to recently experienced distances and turns; (ii) whether effects of experience can be specifically attributed to changes in the encoding process, and if so, what changes; and (iii) whether the encoding process represents distances and turns in the individual paths without considering their spatial relationship to one another (i.e., an object-centered representation). Testing these issues allows us to evaluate and develop the model.

Abstract. This paper presents analysis and follow up experiments based on previous work where a neurally controlled simulated agent was evolved to navigate using path integration (PI). Specifically, we focus on one agent, the best one produced, and investigate two interesting features. Firstly, the agent stores its current coordinates in two leaky integrators, whose leakage is partially compensated for by a normalisation mechanism. We use a comparison between four network topologies to test if this normalised leakage mechanism is adaptive for the agent. Secondly, the controller generates efficient searching behaviour in the vicinity of its final goal. We begin an analysis of the dynamical system (DS) responsible for this, starting from a simple three variable system. 1

In two experiments, subjects traveled through virtual mazes, encountering target objects along the way. Their task was to indicate the direction to these target objects from a terminal location in the maze (from which the objects could no longer be seen). Subjects controlled their motion through the mazes using three locomotion modes. In the Walk mode, subjects walked normally in the experimental room. For each subject, body position and heading were tracked, and the tracking information was used to continuously update the visual imagery presented to the subjects on a head-mounted display. This process created the impression of immersion in the experimental maze. In the Visual Turn mode subjects moved through the environment using a joystick to control their turning. The only sensory information subjects received about rotation and translation was that provided by the computer-generated imagery. The Real Turn mode was midway between the other two modes, in that subjects physically turned in place to steer while translating in the virtual maze; thus translation through the maze was signaled only by the computer-generated imagery, whereas rotations were signaled by the imagery as well as by proprioceptive and vestibular information. The dependent measure in the experiment was the absolute error of the subject's directional estimate to each target from the terminal location. Performance in the Walk mode was significantly better than in the Visual Turn mode but other trends were not significant. A secondary finding was that the degree of motion sickness depended upon locomotion mode, with the lowest incidence occurring in the Walk mode. Both findings suggest the advisability of having subjects explore virtual environments using real rotations and translations in tasks invol...