Abstract-Olfaction is a long distance sense, which is widely used by animals for foraging or reproductive activities. Olfaction plays a significant role in natural life of most animals. For some animals, olfactory cues are far more effective than visual or auditory cues in search for objects such as foods and nests. Although chemical sensing is far simpler than vision or hearing, navigation in a chemical diffusion field is still not well understood. Therefore, this powerful primary sense has rarely been used inside the robotics community. This paper presents an effective olfactory-based planning and search algorithms for using on mobile robots. Olfactory-based mobile robots use odors as a guide to navigate and track in the unknown environments. The planning algorithms are based on Bayesian inference theory and artificial potential field methods. Inputs to the algorithms include the measured flow and the detection or non-detection events that happened at the robot location. This methodology results in algorithms for predicting likelihood of source location versus position. The robot would then optimize a desired trajectory to navigate in the odor plume and locate the odor source location
