Dynamics parametrically controlled by image correlations organize robot navigation

Abstract.

Behavior-based robot designs confront the problem of how different elementary behaviors can be integrated. We address two aspects of this problem: the stabilization of behavioral decisions that are induced by changing sensory information and the fusion of multiple sources of sensory information. The concrete context is homing and obstacle avoidance in a vision-guided mobile robot. Obstacle avoidance is based on extracting time-to-contact information from optic flow. A dynamical system controls heading direction and velocity. Time-to-contact estimates parametrically control this dynamical system, the attractors of which generate robot movement. Decisions come about through bifurcations of the dynamics and are stabilized through hysteresis. Homing is based on image correlations between memorized and current views. These control parametrically a dynamics of ego-position estimation, which converges in closed loop so as to position the robot at the home position. Unreliable visual information and more continous open-loop dead-reckoning information are integrated within this dynamics. This permits vision-based homing, but also stabilizes the behavior during periods of absent or erroneous visual information through the internal state of the dynamical system. The navigation scheme is demonstrated on a robot platform in real time.