etd AT Indian Institute of Science >
Division of Mechanical Sciences >
Aerospace Engineering (aero) >
Please use this identifier to cite or link to this item:
|Title: ||Odor Source Localization Using Swarm Robotics|
|Authors: ||Thomas, Joseph|
|Advisors: ||Ghose, Debasish|
|Keywords: ||Swarm Robotics|
Odor Source Localization
Glowworm Swarm Optimization (GSO) Algorithms
Odor Source Models
Odor Source Localization - Algorithms
|Submitted Date: ||Dec-2008|
|Series/Report no.: ||G22949|
|Abstract: ||Locating an odor source in a turbulent environment, an instinctive behavior of insects such as moths, is a nontrivial task in robotics. Robots equipped with odor sensors ﬁnd it diﬃcult to locate the odor source due to the sporadic nature of odor patches in a turbulent environment. In this thesis, we develop a swarm algorithm which acquires information from odor patches and utilizes it to locate the odor source. The algorithm utilizes an intelligent integration of the chemotaxis, anemotaxis and spiralling approaches, where the chemotactic behavior is implemented by the recently proposed Glowworm Swarm Optimization (GSO) algorithm. Agents switch between chemotactic, anemotactic, and spiralling modes in accordance with the information available from the environment for optimal performance. The proposed algorithm takes full advantage of communication and collaboration between the robots. It is shown to be robust, eﬃcient and well suited for implementation in olfactory robots. An important feature of the algorithm is the use of maximum concentration encountered in the recent past for navigation, which is seen to improve algorithmic performance signiﬁcantly.
The algorithm initially assumes agents to be point masses, later this is modiﬁed for robots and includes a gyroscopic avoidance strategy. A variant of the algorithm which does not demand wind information, is shown to be capable of locating odor sources even in no wind environment. A deterministic GSO algorithm has been proposed which is shown capable of faster convergence. Another proposed variant, the push pull GSO algorithm is shown to be more eﬃcient in the presence of obstacle avoidance.
The proposed algorithm is also seen capable of locating odor source under varying wind conditions. We have also shown the simultaneous capture of multiple odor sources by the proposed algorithm. A mobile odor source is shown to be captured and tracked by the proposed approach. The proposed approaches are later tested on data obtained from a realistic dye mixing experiment. A gas source localization experiment is also carried out in the lab to demonstrate the validity of the proposed approaches under real world conditions.|
|Appears in Collections:||Aerospace Engineering (aero)|
Items in etd@IISc are protected by copyright, with all rights reserved, unless otherwise indicated.