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.

Locating odour sources with mobile robots is a difficult task with many real world applications. Over the years, researchers have devised bio-inspired and cognitive methods to enable mobile robots to fulfil this task. One of the most popular cognitive approaches is Infotaxis, which computes a probability map for the location of the chemical source and, on each time step, moves the robot in the direction that minimises the entropy of that probability map. The main difficulty for applying Infotaxis in the real world is selecting proper values for the parameters of its internal gas dispersion model, as it has been shown that its performance is greatly influenced by the accuracy of said model. This work proposes a Genetic Algorithm for optimising those parameters for specific environments. The proposed method is applied to environments with distinct wind and odour dispersion characteristics and the resulting parameters are compared. Moreover, the performance of Infotaxis is compared to that of reactive search strategies evolved by Geometric Syntactic Genetic Programming. The statistically validated results show that the evolved reactive strategies achieve equivalent success rates to Infotaxis, while being significantly faster. Real world experiments conducted in a controlled wind tunnel validated the simulation results.

This study is partially supported by FEDER Funds through the COMPETE - Programa Operacional Fatores de Competitividade and by national funds by FCT – Fundacao para a Ciencia e Tecnologiain the scope of the Project: FCOMP-01-0124-FEDER-022674, and GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME-GIN/120761/2010), financed by national funds by FCT / MCTES, and co-funded by the European Social Development Fund (FEDER) through the COMPETE - Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124-FEDER-020609.