Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions

This paper proposes a strategy for performing an efficient autonomous search to find an emitting source of sporadic cues of noisy information. We focus on the search for a source of unknown strength, releasing particles into the atmosphere where turbulence can cause irregular gradients and intermittent patches of sensory cues. Bayesian inference, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source location and strength in response to sensor measurements. Posterior sampling is then used to approximate a reward function, leading to the manoeuvre to where the entropy of the predictive distribution is the greatest. As it is developed based on the maximum entropy sampling principle, the proposed framework is termed as Entrotaxis. We compare the performance and search behaviour of Entrotaxis with the popular Infotaxis algorithm, for searching in sparse and turbulent conditions where typical gradient-based approaches become inefficient or fail. The algorithms are assessed via Monte Carlo simulations with simulated data and an experimental dataset. Whilst outperforming the Infotaxis algorithm in most of our simulated scenarios, by achieving a faster mean search time, the proposed strategy is also more computationally efficient during the decision making process.