Odor-Based Navigational Strategies for Mobile Agents

Abstract

Although most species are sensitive to various chemicals, and olfactory skills such as search strategies for finding nutritious substance are seemingly simple, these basic skills are still not fully understood. Traditionally, chemotaxis has been considered as the fundamental chemosensory navigational mechanism for most species. Previous studies have demonstrated, however, that biased random walk is the more fundamental navigational strategy in various types of diffusion fields. Biased random walk is a robust and slow search process, but it has been shown that its efficiency can be enhanced if it is combined with chemotaxis. The present article summarizes previous findings of the authors in olfactory navigation and extends the work to searching in dynamic flow fields, including turbulence. In addition, a cooperative, multi-agent search method has been investigated and shown to be successful in enhancing search efficiency. The significance of these findings is discussed in the context of future plans to implement these strategies in experimental mobile robots.

A systematic approach to the problem of odour source localisation

“…Inspired by Berg and Brown's (1972) study, we introduced biased random walk strategies at a macroscopic scale for mobile robots and showed that BRW is a robust and yet more efficient strategy than chemotaxis in unstable and noisy chemical fields (Kadar and Virk, 1998a). In addition to locating the static point odour source in unstable chemical fields, BRW has also been assessed for moving targets (Kadar and Virk, 1998c), odour trails (Virk and Kadar, 2000) and turbulent plumes (Lytridis et al, 2001). In Virk et al's (1998) study, a fuzzy logicbased sensor fusion approach demonstrated that the BRW algorithm could be combined with obstacle avoidance based on other traditional distal sensors (acoustic, infrared, etc.…”

“…Biological studies have already demonstrated the use of various search methods (e.g., chemotaxis and biased random walk), but robotics research could provide new ways to investigate principles of olfactory-based search skills (Webb, 2000;Grasso, 2001). In previous studies on odour source localisation, we have tested three biologically inspired search strategies: chemotaxis, biased random walk, and a combination of these methods (Kadar and Virk, 1998;Lytridis et al, 2001). The main objective of the present paper is to demonstrate how simulation and robot experiments could be used conjointly to systematically study these search strategies.…”

Evidence for olfactory search in wandering albatross, Diomedea exulans

“…While previous studies have shown that wandering albatrosses forage over both oceanic (depths of 2,000 m or more) and neritic (depths of 2,000 m or less) zones by using both foraging-in-flight and sit-and-wait strategies (11), no study has ever detailed fine-scale movement of any procellariiform at sea with a view toward investigating the sensory basis of foraging, particularly with respect to odor tracking. How animals track odors in water and air is, however, a topic of considerable broader interest with respect to developing algorithms for plume-following behavior at various spatial scales (12,13).…”

Towards 3-D distributed odor source localization: An extended graph-based formation control algorithm for plume tracking

“…Leveraging multiple robots for distributed search has its own peculiarities, many of which have been explored during the last decade. Multirobot search algorithms proposed in the literature include biasing expansion swarm approaches [11], biased random walk [12], particle swarm optimization [13], gradient climbing techniques [14], infotaxis [15], probabilistic reasoning [16], search through exploration [17], physics-based swarming [18], attraction-repulsion swarming [19], and formation-based algorithms [20], [21].…”