Brett R. Fajen

Behavioral Dynamics of Steering, Obstacle Avoidance, and Route Selection

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 goal (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.

Visual navigation and obstacle avoidance using a steering potential function

Abstract Humans have a remarkable ability to navigate using only vision, but mobile robots have not been nearly as successful. We propose a new approach to vision-guided local navigation, based upon a model of human navigation. Our approach uses the relative headings to the goal and to obstacles, the distance to the goal, and the angular width of obstacles, to compute a potential field over the robot heading. This potential field controls the angular acceleration of the robot, steering it towards the goal and away from obstacles. Because the steering is controlled directly, this approach is well suited to local navigation for nonholonomic robots. The resulting paths are smooth and have continuous curvature. This approach is designed to be used with single-camera vision without depth information but can also be used with other kinds of sensors. We have implemented and tested our method on a differential-drive robot and present our experimental results.

Perceiving possibilities for action: on the necessity of calibration and perceptual learning for the visual guidance of action

Tasks such as steering, braking, and intercepting moving objects constitute a class of behaviors, known as visually guided actions, which are typically carried out under continuous control on the basis of visual information. Several decades of research on visually guided action have resulted in an inventory of control laws that describe for each task how information about the sufficiency of ones current state is used to make ongoing adjustments. Although a considerable amount of important research has been generated within this framework, several aspects of these tasks that are essential for successful performance cannot be captured. The purpose of this paper is to provide an overview of the existing framework, discuss its limitations, and introduce a new framework that emphasizes the necessity of calibration and perceptual learning. Within the proposed framework, successful human performance on these tasks is a matter of learning to detect and calibrate optical information about the boundaries that separate possible from impossible actions. This resolves a long-lasting incompatibility between theories of visually guided action and the concept of an affordance. The implications of adopting this framework for the design of experiments and models of visually guided action are discussed.

Visual Guidance of Intercepting a Moving Target on Foot

How do people walk to a moving target, and what visual information do they use to do so? Under a pursuit strategy, one would head toward the targets current position, whereas under an interception strategy, one would lead the target, ideally by maintaining a constant target-heading angle (or constant bearing angle). Either strategy may be guided by the egocentric direction of the target, local optic flow from the target, or global optic flow from the background. In four experiments, participants walked through a virtual environment to reach a target moving at a constant velocity. Regardless of the initial conditions, they walked ahead of the target for most of a trial at a fairly constant speed, consistent with an interception strategy (experiment 1). This behavior can be explained by trying to maintain a constant target-heading angle while trying to walk a straight path, with transient steering dynamics. In contrast to previous results for stationary targets, manipulation of the local optic flow from the target (experiment 2) and the global optic flow of the background (experiments 3 and 4) failed to influence interception behavior. Relative motion between the target and the background did affect the path slightly, presumably owing to its effect on perceived target motion. We conclude that humans use an interception strategy based on the egocentric direction of a moving target.

Behavioral dynamics of intercepting a moving target.

From matters of survival like chasing prey, to games like football, the problem of intercepting a target that moves in the horizontal plane is ubiquitous in human and animal locomotion. Recent data show that walking humans turn onto a straight path that leads a moving target by a constant angle, with some transients in the target-heading angle. We test four control strategies against the human data: (1) pursuit, or nulling the target-heading angle β, (2) computing the required interception angle

Affordance-Based Control of Visually Guided Action

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Learning to control collisions: the role of perceptual attunement and action boundaries

(3) constant target-heading angle, or nulling change in the target-heading angle