A dynamical model of visually-guided steering, obstacle avoidance, and route selection
Using a biologically-inspired model, we show how successful route selection through a cluttered environment can emerge from on-line steering dynamics, without explicit path planning. The model is derived from experiments on human walking performed in the Virtual Environment Navigation Lab (VENLab) at Brown. We find that goals and obstacles behave as attractors and repellors of heading, the direction of locomotion, for an observer moving at a constant speed. The influence of a goal on turning rate increases with its angle from the heading and decreases exponentially with its distance; the influence of an obstacle decreases exponentially with angle and distance. Linearly combining goal and obstacle terms allows us to simulate paths through arbitrarily complex scenes, based on information about obstacles in view near the heading direction and a few meters ahead. We simulated the model on a variety of scene configurations and observed generally efficient routes, and verified this behavior on a mobile robot. Discussion focuses on comparisons between dynamical models and other approaches, including potential field models and explicit path planning. Effective route selection can thus be performed on-line, in simple environments as a consequence of elementary behaviors for steering and obstacle avoidance.
obstacle avoidance path planning visual control of locomotion optic flow robot navigation