Reactive Searching and Infotaxis in Odor Source Localization

Abstract

Male moths aiming to locate pheromone-releasing females rely on stimulus-adapted search maneuvers complicated by a discontinuous distribution of pheromone patches. They alternate sequences of upwind surge when perceiving the pheromone and cross- or downwind casting when the odor is lost. We compare four search strategies: three reactive versus one cognitive. The former consist of pre-programmed movement sequences triggered by pheromone detections while the latter uses Bayesian inference to build spatial probability maps. Based on the analysis of triphasic responses of antennal lobe neurons (On, inhibition, Off), we propose three reactive strategies. One combines upwind surge (representing the On response to a pheromone detection) and spiral casting, only. The other two additionally include crosswind (zigzag) casting representing the Off phase. As cognitive strategy we use the infotaxis algorithm which was developed for searching in a turbulent medium. Detection events in the electroantennogram of a moth attached to a robot indirectly control this cyborg, depending on the strategy in use. The recorded trajectories are analyzed with regard to success rates, efficiency, and other features. In addition, we qualitatively compare our robotic trajectories to behavioral search paths. Reactive searching is more efficient (yielding shorter trajectories) for higher pheromone doses whereas cognitive searching works better for lower doses. With respect to our experimental conditions (2 m from starting position to pheromone source), reactive searching with crosswind zigzag yields the shortest trajectories (for comparable success rates). Assuming that the neuronal Off response represents a short-term memory, zigzagging is an efficient movement to relocate a recently lost pheromone plume. Accordingly, such reactive strategies offer an interesting alternative to complex cognitive searching.

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].…”

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

“…Among the search methods, chemotaxis and anemotaxis are model-independent, which is a drawback if the medium is turbulent [15 • ]. The latter causes disconnected patches of chemical substances with low or high dose, thus making it more difficult to get a good gradient estimation of the scalar field.…”

“…Notice that experimental results such as odor tracing in a wind tunnel [10 • ], [11,12,13,14], [15 • ], underground gas leakage tracing in a and pool [7], and source tracing in a controlled water reservoir [20,23], were all carried out in controlled environments. On the other Figure 1 (adapted from [29]): Example of chemotaxis of three agents using a leader-follower approach.…”

“…Several studies exploited this method as reported in [34 •• ], see figure 3, and references therein. In a different study [15 • ], inspired by moths, the authors attached an electroantennogram of a moth to a robot to indirectly control it. They compared four search strategies, three reactive (combination of upwind surge, spiral casting and crosswind [zigzag] casting) [48], and infotaxis [47].…”

Chemical Plume Tracing and Mapping via Swarm Robots

“…The CPT methods can be roughly classified into: anemotaxis (Belanger et al, 1996;Hayes et al, 2002), chemotaxis (Grasso et al, 1999), chemoanemotaxis (Li et al, 2001;Grasso and Atema, 2002;Wu et al, 2013), fluxotaxis (Zarzhitsky et al, 2005), and infotaxis (Vergassola et al, 2006;Voges et al, 2014). The fact that various biological entities use olfactory-based search with high degrees of success has prompted research into the design of autonomous vehicles capable of olfactory based search and chemical plume tracing.…”