Improving Agent Localisation Through Stereotypical Motion

Abstract

When bees and wasps leave the nest to forage, they perform orientation or learning flights. This behaviour includes a number of stereotyped flight manoeuvres mediating the active acquisition of visual information. If we assume that the bee is attempting to localise itself in the world with reference to stable visual landmarks, then we can model the orientation flight as a probabilistic Simultaneous Localisation And Mapping (SLAM) problem. Within this framework, one effect of stereotypical behaviour could be to make the agent’s own movements easier to predict. In turn, leading to better localisation and mapping performance. We describe a probabilistic framework for building quantitative models of orientation flights and investigate what benefits a more reliable movement model would have for an agent’s visual learning.