The poster presents a very simple approach for source and boundary detection using biased random walks (inspired from bacterial motion) which could be a novel strategy for robotic sensor network problems

Amit Dhariwal

Ari Requicha

David Caron

Gaurav Sukhatme

eScholarship - University of California

UCLAPostersTitleBacteria-Inspired Motion Strategies for Robotic Sensor NetworksPermalinkhttps://escholarship.org/uc/item/06k6p7pzAuthorsAmit DhariwalGaurav SukhatmeAri Requichaet al.Publication Date2003eScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaProblem Description: Locate and Track Dynamic Gradient SourcesSimulation ResultsBacteria-inspired Motion Strategies for Robotic Sensor NetworksAmit Dhariwal, Gaurav S. Sukhatme, Aristides Requicha, David CaronRobotic Embedded Systems Lab, USC - http://www-scf.usc.edu/~dhariwal/dpstIntroduction: Locating Gradient Sources in a Computationally Simple MannerProblem Characteristics Characteristics of Bacterial MotionSolution CriteriaCenter for Embedded Networked SensingUCLA – UCR – Caltech – USC – CSU – JPL – UC Merced• Assumption: The source generates a gradient which can be sensed by the robots• Dynamic source: The intensity of the gradient generated by a source may vary over time• Source Location: The gradient source location may vary over time• Multiple Gradient Sources: There can be multiple gradient sources near the robots• Gradient Types: Temperature, Light intensity, Salinity (conductivity), pH, Opacity, Minerals etc.• Produced through the action of flagella• Move towards nutrient sources by following gradients• Move towards attractive stimuli and away from harmful substances in a process known as Chemotaxis– It consists of a straight run of an average duration which is followed by an uncoordinated tumble which randomizes the direction of the next run– Negative Chemotaxis results in a random tumbling motion– Positive Chemotaxis results in an increase in the run duration• Simplicity• Minimality in sensing/memory/communication/processing• Insensitive to errors in sensing• Should not require localization• Should have a small form factor and be scalable• Ocean coast monitoring, Distributed plume source tracking, Detecting oil spill boundariesApplication AreasLimitations• The system takes time to converge to the gradient source. This makes it unsuitable for applications where the source moves rapidly.Conclusions• Can effectively track one or more gradient sources• Can adapt to dynamic and mobile gradient sources• Modest tolerance to errors in sensor measurements (only the difference in readings is used to make a decision, not the absolute sensor readings)• Requires minimal amount of memory/sensorExperiments on the Robomote PlatformThe Algorithm: A Biased Random Walk• Model the world as a grid of dimensions 2000 * 2000 Units• Randomly initialize source(s) of gradient(s)• A set of robot nodes initialized at random locations in the grid• The nodes move an average distance of 10 units (also called the Mean Free Path (MFP) of the system) followed by an uncoordinated tumble (random direction change)• A bias factor which determines the increase in the MFP in case a positive gradient change is observedOngoing and Future Work•Effectiveness of Biased Random Walk in Boundary Detection•Surface Water Experiments for Gradient Source DetectionFig 3. Distance traveled by bacteria from their initial position over time•Schematic figure goes hereFig 4. Progress towards the Gradient Source as a function of time under different bias conditionsFig 2. Random Walk vs. Biased Random WalkFig 1. Gradient Source (Green); Robot Nodes (Red); 

Bacteria-Inspired Motion Strategies for Robotic Sensor Networks

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