Computational studies of exploration by smell

Abstract

Research on exploratory and searching behavior of animals and robots has attracted an increasing amount of interest recently. Existing works have focused mostly on exploratory behavior guided by vision and audition. Research on smell-guided exploration has been lacking, even though animals may use the sense of smell more widely than sight or hearing to search for food and to evade danger. This article contributes to the study of smell-guided exploration. It describes a series of increasingly complex neural networks, each of which allows a simulated creature to search for food and to evade danger by using smell. Other behaviors such as obstacle negotiation and risk taking emerge naturally from the creature's interaction with the environment. Comparative studies of these networks show that there is no significant performance advantage for a creature to have more than two sensors. This result may help to explain why real animals have only one or two smell-sensing organs.