Behavioral dynamics of steering, obstable avoidance, and route selection.

Abstract

The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.

Controlling steering and judging heading: Retinal flow, visual direction, and extraretinal information.

“…We make an initial proposal in the Appendix, by combining them in a second-order model, based on the proposals of Fajen and Warren (2003), that acts as a point attractor toward the point of fixation. If the observer fixates the intended target, RF, ER, and VD information can all be used to provide independent estimates of understeer or oversteer.…”

“…We do not deny that these tasks could be completed by recovering heading and its rate of change, but this would still leave the problem of how the observer uses a heading angle to plan a path in three-dimensional space. Fajen and Warren (2003) proposed an equivalent point attractor model that acts to null the heading offset, but this has the additional requirement of recovering target distance. Our approach does not require the estimates of instantaneous heading or target distance, and in this respect we consider it a parsimonious model for "simple" steering that has the flexibility to be extended to complex, planned trajectories through the use of active gaze.…”

“…There are, therefore, three potential sources of information through which gaze rotation can be detected. These can be combined using a simple point attractor (Fajen & Warren, 2003), to provide a robust model for visually guided steering: θ υ β β β µ θ…”

Planning of spatially-oriented locomotion following focal brain damage in humans: A pilot study

“…Other approaches do not assume such computational modules and propose a direct use of optic flow (alone or in combination with other visual variables) to guide locomotion and also allow for the prediction of a large range of locomotor trajectories [21]. The spatial stereotypy of trajectories observed during blindfolded locomotion questions the relevance of such approaches.…”

A Robust Algorithm for Local Obstacle Avoidance

“…The remarkable methods for these kind of robot navigation are Borestein and Koren's Vector Field Histogram (VFH) [7][8], dynamic window approach [6] and the curvature lane approach [4]. Fajen and Warren have proposed a model for obstacle avoidance by humans [5] which was again modified by Hammer et al [3].…”