Particle swarm-based olfactory guided search

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

doi:10.1007/s10514-006-7567-0

Cited by 179 publications

(125 citation statements)

References 21 publications

Supporting

Mentioning

124

Contrasting

Unclassified

Order By: Relevance

“…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%

See 1 more Smart Citation

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

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.

show abstract

“…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%

“…In the real experiment the robots could locate the fire sources during the exploration. The performance of fire source detection has been addressed in previous studies [17], [19].…”

Section: The Real World Experimentsmentioning

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

show abstract

“…Most of the related works concerning olfactory search have focused on either single robot experiments [1,2,3] or multiple robots operating in open areas free of obstacles [4,5,6] with a background fluid flow. The odor is carried downwind originating from the source forming a plume.…”

Section: Multi-robot Olfactory Searchingmentioning

confidence: 99%

Multi-robot olfactory search in structured environments

Marjovi

Marques

2011

Robotics and Autonomous Systems

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Experiments have also been conducted with multiple robots [10,17,13], acting both independently and cooperatively. Other approaches include Braitenberg-type control [16], probabilistic inference [28,17,15] and meta-heuristic optimization methods [1,2,3,12,21].…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Formation Algorithm for Odor Plume Tracing

Soares

Aguiar

Pascoal

et al. 2016

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

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.

show abstract

Particle swarm-based olfactory guided search

Cited by 179 publications

References 21 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

Contact Info

Product

Resources

About