Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting

Cheng, Song; He, Yuqing; Ristić, Branko; Lei, Xiaokang

doi:10.1016/j.robot.2019.103414

Cited by 28 publications

(12 citation statements)

References 28 publications

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“…Song et al . [88] presented a different collaborative method, which applied the weighted social Bayesian estimation to make full use of all the measurements at each step. The measurements were fused according to a cognition‐difference criterion measured by KL‐divergence.…”

Section: Probabilistic Inference Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Probabilistic Inference Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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“…Gradient-based methods are also widely considered in source-seeking applications, mainly divided into singleagent methods and multi-agents cooperative ones. As for the single agent, in [19][20][21], researchers used random gradient estimation to make the agent randomly move in the signal field, measure the spatial information of the signal field, and calculate the gradient direction of the signal field. Krstic et al [19,20] applied extreme value search control to make a single agent move to the local signal maximum in a noisefree signal field.…”

Section: Related Workmentioning

confidence: 99%

“…As for the single agent, in [19][20][21], researchers used random gradient estimation to make the agent randomly move in the signal field, measure the spatial information of the signal field, and calculate the gradient direction of the signal field. Krstic et al [19,20] applied extreme value search control to make a single agent move to the local signal maximum in a noisefree signal field. Anatasov et al [21] made a single agent calculate the gradient by random movement and drove the agent to signal source with gradient information.…”

Section: Related Workmentioning

confidence: 99%

Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network

Rong

Chen

et al. 2021

Int J Wireless Inf Networks

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Source seeking problem has been faced in many fields, especially in search and rescue applications. We proposed a virtual structure-based spatial-temporal method to realize cooperative source seeking using multi-agents. Spatially, a circular formation is considered to gather collaborative information and estimate the gradient direction of the formation center. In terms of temporal information, we use the formation positions in time sequence to construct a virtual structure sequence. Then, we fuse the sequential gradient as a whole. Experimental results show that, compared with state-of-the-art, the proposed method can quickly and efficiently find the source so that the formation can minimize the movement distance during the moving process and increase the efficiency of source seeking. Numerical simulations confirm the efficiency of the scheme put forth. Compared with state-of-the-art source-seeking methods, the iterative steps of our proposed method are reduced by 20%, indicating that the method can find the signal source with higher efficiency and lower energy consumption, and better robustness.

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“…Various solutions to odor source localization have been studied. Probabilistic GSL strategies [9], [10] usually internally maintain a map with the probabilities for the odor source location and often simulate the gas distribution. This is computationally challenging for nano quadcopters, a situation that deteriorates when they have to operate in environments with complex shapes, obstacles and a complex airflow field.…”

Section: Introductionmentioning

confidence: 99%

Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments

Duisterhof¹,

Li²,

Burgués³

et al. 2021

Preprint

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Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. In this work, we propose a novel bug algorithm named 'Sniffy Bug', which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in an unknown, cluttered and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based procedure. We evolve all the parameters of the bug (and PSO) algorithm, using our novel simulation pipeline, 'AutoGDM'. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, realworld environments. Videos: https://bit.ly/37MmtdL

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Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting

Cited by 28 publications

References 28 publications

Recent Progress and Trend of Robot Odor Source Localization

Recent Progress and Trend of Robot Odor Source Localization

Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network

Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments

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