Robot Odor Localization: A Taxonomy and Survey
International Journal of Robotics Research
Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Reactive planning for olfactory-based mobile robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multiple robots plume-tracing in open space obstructed environments
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Robot algorithms for localization of multiple emission sources
ACM Computing Surveys (CSUR)
A learning particle swarm optimization algorithm for odor source localization
International Journal of Automation and Computing
Towards a multi-peclet number pollution monitoring algorithm
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Robotics and Autonomous Systems
Evolutionary robotics approach to odor source localization
Neurocomputing
Autonomous Agents and Multi-Agent Systems
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This paper addresses the problem of estimating a likelihood map for the location of the source of a chemical plume using an autonomous vehicle as a sensor probe in a fluid flow. The fluid flow is assumed to have a high Reynolds number. Therefore, the dispersion of the chemical is dominated by turbulence, resulting in an intermittent chemical signal. The vehicle is capable of detecting above-threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. This paper reviews instances of biological plume tracing and reviews previous strategies for a vehicle-based plume tracing. The main contribution is a new source-likelihood mapping approach based on Bayesian inference methods. Using this Bayesian methodology, the source-likelihood map is propagated through time and updated in response to both detection and nondetection events. Examples are included that use data from in-water testing to compare the mapping approach derived herein with the map derived using a previously existing technique