Bioinspired visual guidance in turbid underwater environment

Abstract

In this paper, an obstacle avoidance strategy using monocular gray-scale robotic vision for turbid water environments is presented. Biologically inspired by the unique vision system of the cubozoan, or box jellyfish, the proposed obstacle avoidance techniques were designed to be as computationally inexpensive as possible for implementation in a compact autonomous underwater vehicle with on-board processing capabilities. The sharp contrast reduction in turbid waters between obstacles and the surrounding environment is leveraged as a semi-reliable measure of relative distance between obstacles to form an evasion response based on obstacle priority. This contrast reduction model can be applied to both underwater and aerial vehicles depending on environmental turbidity and the relative distance between obstacles. In order to test this bioinspired approach, the proposed obstacle avoidance algorithm is implemented on a simple, low-power digital signal processor. It is shown that using contrast as a sole depth cue in turbid underwater environments is suitable for the detection of large, stationary obstacles.