This thesis presents the work towards analyzing the odour source localization problem found in nature as a suitable analogy for planetary exploration missions. The contributions are models of two environments for which the analogy may be relevant, the training of a recurrent neural network that replicates simple forms of odour source locational strategies found in nature, and the analysis and advancement of a source likelihood map algorithm for predicting the location of a chemical source given individual detection events. Two missions that may be suitable for using the odour source localization analogy are locating methane sources on Mars and choosing a landing site for exploration of Enceladus' postulated subsurface ocean. The details in training recurrent neural networks for simple moth behaviours is shown and the results using the dynamic Mars methane plumes are discussed. The advancement of a binary chemical detection and source-localization method is shown, which improves accuracy.