A study of cognitive strategies for an autonomous search

Ristić, Branko; Skvortsov, Alex; Gunatilaka, Ajith

doi:10.1016/j.inffus.2015.06.008

Cited by 89 publications

(104 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As found for the simulated plume in Figure 6, and in our previous work [23], the search times for the experimental plume can be accurately modelled by an inverse Gaussian distribution.…”

Section: Numerical Resultssupporting

confidence: 80%

“…The empirical search time samples were obtained with the source location fixed at

(X_{0}, Y_{0}) = (187.5, 187.5)

and the initial robot formation centroid at

false[187.5, - 187.5 false]

. As found in our previous work [23], the search time for a single search platform is well modelled by an inverse Gaussian. The model, however, does not hold as strongly for

N = 5

searching platforms, especially for shorter search times.…”

Section: Numerical Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Ristić

Angley

Moran

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao–Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics.

show abstract

“…As found for the simulated plume in Figure 6, and in our previous work [23], the search times for the experimental plume can be accurately modelled by an inverse Gaussian distribution.…”

Section: Numerical Resultssupporting

confidence: 80%

“…The empirical search time samples were obtained with the source location fixed at

(X_{0}, Y_{0}) = (187.5, 187.5)

and the initial robot formation centroid at

false[187.5, - 187.5 false]

. As found in our previous work [23], the search time for a single search platform is well modelled by an inverse Gaussian. The model, however, does not hold as strongly for

N = 5

searching platforms, especially for shorter search times.…”

Section: Numerical Resultsmentioning

confidence: 99%

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Ristić

Angley

Moran

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…An interesting approach to guide a robot to a gas source was suggested in Vergassola et al (), where an information‐based reward was formulated. This new method received considerable interest in the area and has lead numerous researchers to propose modifications and extensions to the algorithm (Masson, ; Moraud & Martinez, ; Ristic, Skvortsov, & Gunatilaka, ). Experimental studies of such an approach have so far been limited to indoor areas with artificially generated wind (Voges et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, information‐based guidance methods have been proposed to guide the robot to the expected most informative locations to take measurements. These approaches have been proposed to estimate the source terms of radiological releases (Ristic, Morelande, & Gunatilaka, ), a diffusive release (Hutchinson, Oh, & Chen, ; Ristic et al, ) and to complement an existing static network of detectors (, ). In Hutchinson, Liu, et al (), an information‐based STE method was implemented on a ground robot and assessed in indoor experiments where fans were used to simulate a wind field.…”

Section: Related Workmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

View full text Add to dashboard Cite

Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

show abstract

“…From a theoretical stand-point, this method is based on information principles to maximize the entropy reduction. Several studies exploited this method as reported in [34 •• ], see figure 3, and references therein. In a different study [15 • ], inspired by moths, the authors attached an electroantennogram of a moth to a robot to indirectly control it.…”

Section: Entropy Reduction (Infotaxis)mentioning

confidence: 99%

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

Bayat

Crasta

Crespi

et al. 2017

Current Opinion in Biotechnology

140

View full text Add to dashboard Cite

Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles.

show abstract

A study of cognitive strategies for an autonomous search

Cited by 89 publications

References 23 publications

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

Contact Info

Product

Resources

About