Locating odour sources with mobile robots is a difficult task with many applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. Cognitive approaches are effective in large spaces, but computationally heavy. On the other hand, bio-inspired ones are lightweight, but they are only effective in the presence of frequent stimuli. One of the most popular cognitive approaches is Infotaxis, which iteratively computes a probability map of the source location. Another strand of work uses Genetic Programming to produce complete search strategies from bio-inspired behaviours. This work combines the two approaches by allowing Genetic Programming to evolve search strategies that include infotactic and bio-inspired behaviours.
The proposed method is tested in a set of environments with distinct airflow and chemical dispersion patterns. Its performance is compared to that of evolved strategies without infotactic behaviours and to the standard infotaxis approach. The statistically validated results show that the proposed method produces search strategies that have significantly higher success rates, whilst being faster than those produced by any of the original approaches. Moreover, the best evolved strategies are analysed, providing insight into when infotaxis is more beneficial.