ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Theoretical analysis of three bio-inspired plume tracking algorithms
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Multi-robot based chemical plume tracing with virtual odor-source-probability sensor
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Multiple robots plume-tracing in open space obstructed environments
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Application notes: memetic mission management
IEEE Computational Intelligence Magazine
WSEAS TRANSACTIONS on SYSTEMS
Robots implementation for odor source localization using PSO algorithm
WSEAS Transactions on Circuits and Systems
A learning particle swarm optimization algorithm for odor source localization
International Journal of Automation and Computing
A PSO-based algorithm designed for a swarm of mobile robots
Structural and Multidisciplinary Optimization
Hi-index | 0.00 |
This paper provides a combination of chemotaxic and anemotaxic modeling, known as odor-gated rheotaxis (OGR), to solve real-world odor source localization problems. Throughout the history of trying to mathematically localize an odor source, two common biometric approaches have been used. The first approach, chemotaxis, describes how particles flow according to local concentration gradients within an odor plume. Chemotaxis is the basis for many algorithms, such as particle swarm optimization (PSO). The second approach is anemotaxis, which measures the direction and velocity of a fluid flow, thus navigating "upstream" within a plume to localize its source. Although both chemotaxic and anemotaxic based algorithms are capable of solving overly-simplified odor localization problems, such as dynamic-bit-matching or moving-parabola problems, neither method by itself is adequate to accurately address real life scenarios. In the real world, odor distribution is multi-peaked due to obstacles in the environment. However, by combining the two approaches within a modified PSO-based algorithm, odors within an obstacle-filled environment can be localized and dynamic advection-diffusion problems can be solved. Thus, robots containing this modified particle swarm optimization algorithm (MPSO) can accurately trace an odor to its source