Olfactory searches with limited space perception

Abstract

Various insects and small animals can navigate in turbulent streams to find their mates (or food) from sparse pheromone (odor) detections. Their access to internal space perception and use of cognitive maps still are heavily debated, but for some of them, limited space perception seems to be the rule. However, this poor space perception does not prevent them from impressive search capacities. Here, as an attempt to model these behaviors, we propose a scheme that can perform, even without a detailed internal space map, searches in turbulent streams. The algorithm is based on a standardized projection of the probability of the source position to remove space perception and on the evaluation of a free energy, whose minimization along the path gives direction to the searcher. An internal "temperature" allows active control of the exploration/exploitation balance during the search. We demonstrate the efficiency of the scheme numerically, with a computational model of odor plume propagation, and experimentally, with robotic searches of thermal sources in turbulent streams. In addition to being a model to describe animals' searches, this scheme may be applied to robotic searches in complex varying media without odometry error corrections and in problems in which active control of the exploration/exploitation balance is profitable.biological search | plume tracking | search algorithm T he survival of insects and animals depends on their ability to search and reach for food and mate from the various emitted chemicals in complex varying environments. It is very likely that evolution, regarding search strategies, acted not only on the statistics of different modes of space exploration (1-6) (e.g., generalized levy processes) but also actively on the decision process in relation to the time evolving detected signals. However, a limited number of models tackle with deciphering the information of the signal transported in the environment.Considering search schemes from the signal deciphering point of view shifts the modeling process toward a mix of information, game, and optimal control theory. Hence, a key part of the reasoning is focused on how evolution selected a balance between exploitation of the information accumulated during the search and exploration of the environment (7,8). Among the schemes that are intended to deal with the randomness induced by turbulence and that directly address the exploration/exploitation balance, infotaxis is the most efficient (9-11), i.e., the one exhibiting the lowest average search time and the highest reliability in source reaching. The two key elements of infotaxis are the knowledge by the searcher of both the rate of detection function and the statistics of detection, to infer the probability map of the source position, and the use of entropy as a function to be greedily minimized along the path chosen by the searcher. Infotaxis is robust to various sources of noise, with optimal search efficiency when the model in infotaxis matches the environmental dynamics. However, for an animal,...

Mice Develop Efficient Strategies for Foraging and Navigation Using Complex Natural Stimuli

“…Additionally, we have shown that in this turbulent regime, the odor gradient provides noisy information sufficient for short-range navigation to an odor source. Taken together, our results provide the conceptual framework to combine the recently advanced understanding of odor-driven behavior [8,10,41] and neural coding [42] in rodents with the complex dynamics present in odor plumes in order to define possible algorithms [18,43,44] and neural circuit computations [45] for natural plume tracking [15] during complex decision-making.…”

A study of cognitive strategies for an autonomous search

“…Vergassola et al [10] proposed, for this case, a search strategy (referred to as infotaxis) based on information theoretic principles: infotaxic strategy maximises the expected rate of entropy reduction. This search strategy made a significant impact, resulting in several papers studying its properties and proposing modifications [11][12][13][14][15]. Alternative information theoretic search strategies (collectively referred to as cognitive strategies) have been developed and exploited in the context of searching for radioactive sources [16,17] and a chemical source in the presence of obstacles [18].…”

Decentralised Scalable Search for a Hazardous Source in Turbulent Conditions

“…In the absence of a smooth distribution of concentration (e.g., due to turbulence), this strategy directs the searching robot(s) towards the highest information gain. As a theoretically principled approach, where the source-parameter estimation is carried out in the Bayesian framework and the searching platform motion control is based on the information-theoretic principles, the infotaxic (or cognitive) search strategies have attracted a great deal of interest [3,[14][15][16][17][18][19][20][21][22][23].…”

“…The searching platforms form a moving sensor network, thus enabling the exchange of data and a cooperative behaviour. The multi-robot infotaxis have already been studied in [16,17,20,24]. However, all mentioned references assumed all-to-all (i.e., fully connected) communication network with centralised fusion and control of the searching group.…”