g , heroin), searching for survivors in collapsed buildings,

g., heroin), searching for survivors in collapsed buildings, Vandetanib mw humanitarian de-mining, and fighting against terrorist attacks.The behavior based OSL task can be decomposed into three sub-procedures, namely plume finding, plume traversal, and source declaration, according to Hayes et al. [5], or four sub-procedures according to Li et al. [6], namely finding a plume, tracing the plume, reacquiring the plume, and declaring the source. During the initial phase, contact is made with a plume. Once the plume is detected the robot traces the chemical toward its source. In the final phase the robot locates the source. To our knowledge, until now most research related to OSL focuses on plume tracing, which may be why mobile robot based OSL is also called chemical plume tracing (CPT) [7,8].
The way in which a robot performs each of these phases depends upon the nature of the chemical plume, and the resources available to the robot.The commonly used methods for finding the Inhibitors,Modulators,Libraries chemical plume consist of zigzag [4,6] and spiral [5] motions. Experimental comparison of the spiral, up-flow and down-flow zigzag Inhibitors,Modulators,Libraries strategies conducted in outdoor Inhibitors,Modulators,Libraries natural airflow environments shows that all of these strategies present a high success rate, with the down-flow zigzag strategy consuming the shortest time in finding a plume [9].The traditional plume tracing methods include chemotaxis [10] and anemotaxis [4], which are biologically inspired algorithms. The custom algorithms such as fluxotaxis [8] and infotaxis [11] have also been proposed and tested.
Several insect-inspired chemical plume-tracing algorithms, including surge anemotaxis, Inhibitors,Modulators,Libraries bounded search and counterturning have been compared using a mobile robot [12]. Four reactive robot chemotaxis algorithms, observed in the bacterium E. coli, the silkworm moth Bombyx mori, and the dung beetle Geotrupes stercorarius as well as a gradient-based algorithm, have also Batimastat been implemented and evaluated [10]. Li et al. [13] presented a particle filter algorithm for odor source localization in outdoor time-variant airflow environments.To identify the gas source, Lilienthal and his colleagues [14] adopted an artificial neural network and support vector machine to classify whether or not an object was a gas source from a series of concentration measurements recorded while the robot performed a rotation maneuver in front of it.
Li and his colleagues [6] designed a source declaration logic based on analysis, Monte Carlo simulation, and results of initial field experiments. Li and Meng [15] put forward a three-step selleck chemicals llc single odor source declaration method. Experimental results in indoor airflow environments using three small mobile robots validated the feasibility.Compared with the single-robot search, multiple robots might have at least two advantages: the expected search time could be decreased; and multi-robot systems could provide a greater robustness against hardware failures.

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