Odor Tracking Algorithm and Scent Distribution Model Development for Micro Bio-mimetic Robot
Date Issued
2004
Date
2004
Author(s)
Lee, Yuan-Hwa
DOI
en-US
Abstract
This thesis presents a methodology for developing a search algorithm which directs self-organized and micro-autonomous robotic systems. Then it demonstrates how this algorithm can be applied to the problem of finding one or more than one odor sources in the indoor environment without constant airflow. The search algorithm is applicable to other task domains and the resulting odor localization system can extend the development of a micro-robot. Specifically, this thesis analyzes a basic collective search task for random and coordinated scent search. It also investigates a set of biologically inspired behaviors that permit a micro-robot to traverse an odor distribution environment to its source and describes the common characteristics of successful algorithms.
Collective search and zigzag search are then combined (along with egocentric source declaration) into the full odor localization task which is optimized in simulation. Then, following the design methodology, an odor distribution model with obstacles is presented which is used in simulation to numerically verify the scent search algorithm. Finally, a search behavior is developed for a micro-robotic scent tracking vehicle to collectively “sniff out” locations of high scent concentrations in unknown, geometrically complex environments. The micro-vehicle is assumed to be programmed with guidance and control algorithms. This algorithm is comprised of a sensory compensation sub-algorithm using point source cancellation, a guidance sub-algorithm using spiral surge tracking, zigzag collective search and gradient descent tracking. The concepts of zigzag collective search and point source cancellation are modern concepts introduced within. Simulation results for micro-vehicle are given.
Subjects
即時預測
氣味搜尋仿生體
氣味來源定位
氣味追蹤
氣味補償
Real-time Prediction
Scent compensation
Collective Bio-mimetic Robotics
Odor source tracking
Odor Localization
Type
thesis
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