The Curved Openspace Algorithm and a Spike-Latency Model for Sonar-Based Obstacle Avoidance

The rapid control of a sonar-guided vehicle to pursue a goal while avoiding obstacles has been a persistent research topic for decades. Taking into account the limited field-of-view of practical sonar systems and vehicle kinematics, we propose a neural model for obstacle avoidance that maps the 2-D sensory space into a 1-D motor space and evaluates motor actions while combining obstacles and goal information. A two-stage winner-take-all (WTA) mechanism is used to select the final steering action. To avoid excessive scanning of the environment, an attentional system is proposed to control the directions of sonar pings for efficient, task-driven, sensory data collection. A mobile robot was used to test the proposed model navigating through a cluttered environment using a narrow field-of-view sonar system. We further propose a spiking neural model using spike-timing representations, a spike-latency memory, and a “race-to-first-spike” WTA circuit.