A neural model for sonar-based navigation in obstacle fields

Abstract

The rapid control of sonar-guided vehicles through obstacle fields has been a goal of robotics for decades. How sensory data is represented strongly affects how obstacles and goal information can be combined to select a direction of travel. While typical approaches combine attractive and repulsive effects to directly determine steering, we are investigating an algorithm that evaluates multiple directions simultaneously followed by a winner-take-all (WTA) function which then guides steering. In this paper we describe a neuromorphic VLSI implementation of this algorithm using the inherent echo delay to create a range-dependent gain in a 'race-to-first-spike' neural WTA circuit. The chip was fabricated in a commercially-available 0.5 mum CMOS process and in this paper we present preliminary test results