Olfaction-based mobile robot navigation

Marques, Lino; Nunes, Urbano; Almeida, Anı́bal T. de

doi:10.1016/s0040-6090(02)00593-x

Cited by 177 publications

(128 citation statements)

References 18 publications

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“…The authors have addressed this issue in previous papers [15], [16], [17] and [18]. The last achievement of that research is kheNose.…”

Section: Integration Of Information Collected By Different Robots Intmentioning

confidence: 99%

Multi-Robot Fire Searching in Unknown Environment

Marjovi¹,

Nunes²,

Marques³

et al. 2010

Springer Tracts in Advanced Robotics

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Abstract. Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to locate fire sources in an efficient way. In order to achieve this goal, the robots cooperate in order to individually and simultaneously, explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost/gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots' motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control manner where they share the necessary data to generate a map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulated and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements.

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“…The authors have addressed this issue in previous papers [15], [16], [17] and [18]. The last achievement of that research is kheNose.…”

Section: Integration Of Information Collected By Different Robots Intmentioning

confidence: 99%

Multi-Robot Fire Searching in Unknown Environment

Marjovi¹,

Nunes²,

Marques³

et al. 2010

Springer Tracts in Advanced Robotics

Self Cite

View full text Add to dashboard Cite

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“…Researchers have developed methods that employ combinations and variations of plume acquisition [8,9] and plume upwind following [5,8,10] using reactive control algorithms (a comparison of these methods is in [11]). Most of these approaches are inspired by very simple creatures like moths [3], glowworms [12], etc, therefore they are mostly low-level algorithms making the robot react to the environmental changes based on some defined rules.…”

Section: Multi-robot Olfactory Searchingmentioning

confidence: 99%

Multi-robot olfactory search in structured environments

Marjovi

Marques

2011

Robotics and Autonomous Systems

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This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots . While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.

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“…Many algorithms take inspiration from living creatures, such as bacteria [4] or silkworm moths [20], and generally operate by switching a single robot among a set of simple behaviors. Experiments have also been conducted with multiple robots [10,17,13], acting both independently and cooperatively.…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Formation Algorithm for Odor Plume Tracing

Soares

Aguiar

Pascoal

et al. 2016

Springer Tracts in Advanced Robotics

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Odor plume tracing is a challenging robotics application, made difficult by the combination of the patchy characteristics of odor distribution and the slow response of the available sensors. This work proposes a graph-based formation control algorithm to coordinate a group of small robots equipped with odor sensors, with the goal of tracing an odor plume to its source. This approach makes it possible to organize the robots in arbitrary and evolving formation shapes, with the aim of improving tracking performance. The algorithm was evaluated in a high-fidelity submicroscopic simulator, using different formations and achieving quick convergence and negligible distance overhead in laminar wind flows.

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Olfaction-based mobile robot navigation

Cited by 177 publications

References 18 publications

Multi-Robot Fire Searching in Unknown Environment

Multi-Robot Fire Searching in Unknown Environment

Multi-robot olfactory search in structured environments

A Graph-Based Formation Algorithm for Odor Plume Tracing

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