Target position estimation, target acquisition, and obstacle avoidance

How can low-level autonomous robots with only very simple sensor systems be endowed with cognitive capabilities? Specifically, we consider a system which uses 5 infra-red sensors and 3 light-dependent resistors to acquire targets and avoid obstacles. How can the system be endowed with a continuous representation of target information, complete with subsymbolic memory and a memory decay process? We show that the dynamic approach employed to control the motion of the robot can be extended to the level of representation, if dynamic (neural) fields are used to interpolate sensory information. We show how the system stabilizes the decision, and activates and deactivates memory. Smooth integration of this dynamic target representation with target acquisition and obstacle avoidance is demonstrated.