Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

Wiedemann, Thomas; Vlaicu, Cosmin; Josifovski, Josip; Viseras, Alberto

doi:10.1109/access.2021.3052024

Cited by 22 publications

(19 citation statements)

References 31 publications

(58 reference statements)

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“…This study applied machine learning to process the captured images. Research by [ 23 ] applied RL has in robotics for information gathering in a hazardous gas leakage environment. They implemented an RL framework for the robot to learn how to get gas sources in a known environment.…”

Section: Previous Work Findingsmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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The industrial manufacturing sector is undergoing a tremendous revolution moving from traditional production processes to intelligent techniques. Under this revolution, known as Industry 4.0 (I40), a robot is no longer static equipment but an active workforce to the factory production alongside human operators. Safety becomes crucial for humans and robots to ensure a smooth production run in such environments. The loss of operating moving robots in plant evacuation can be avoided with the adequate safety induction for them. Operators are subject to frequent safety inductions to react in emergencies, but very little is done for robots. Our research proposes an experimental safety response mechanism for a small manufacturing plant, through which an autonomous robot learns the obstacle-free trajectory to the closest safety exit in emergencies. We implement a reinforcement learning (RL) algorithm, Q-learning, to enable the path learning abilities of the robot. After obtaining the robot optimal path selection options with Q-learning, we code the outcome as a rule-based system for the safety response. We also program a speech recognition system for operators to react timeously, with a voice command, to an emergency that requires stopping all plant activities even when they are far away from the emergency stops (ESTOPs) button. An ESTOP or a voice command sent directly to the factory central controller can give the factory an emergency signal. We tested this functionality on real hardware from an S7-1200 Siemens programmable logic controller (PLC). We simulate a simple and small manufacturing environment overview to test our safety procedure. Our results show that the safety response mechanism successfully generates paths without obstacles to the closest safety exits from all the factory locations. Our research benefits any manufacturing SME intending to implement the initial and primary use of autonomous moving robots (AMR) in their factories. It also impacts manufacturing SMEs using legacy devices such as traditional PLCs by offering them intelligent strategies to incorporate current state-of-the-art technologies such as speech recognition to improve their performances. Our research empowers SMEs to adopt advanced and innovative technological concepts within their operations.

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Section: Previous Work Findingsmentioning

confidence: 99%

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Kiangala

Wang

2022

Sensors

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“…This aforementioned work demonstrates that it is possible to find effective multiagent policies with DRL for monitoring. In [7], a similar approach was presented, but for gas leaks localization. This approach used a differential model of the gases to provide a sufficiently accurate model for the deep agent to learn.…”

Section: Previous Workmentioning

confidence: 99%

“…This transforms the monitoring problem into an Informative Path Planning (IPP) problem , which combines the challenge of obtaining the most informative path, and the compliance of ground restrictions and obstacle avoidance. This problem has been previously addressed for an wide set of applications: agricultural characterization [5], for the generation of water quality models [6], the search for gas leaks [7], or the location of contamination sources in radiological environments [8]. Thus, it is presented as a current problem treated from multiple perspectives within engineering.…”

Section: Introductionmentioning

confidence: 99%

Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources Using Autonomous Surface Vehicles

Luis¹,

Reina²,

Toral³

2022

SSRN Journal

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“…The deep learning architecture is used for odor source direction classification and gas source localization [11]. By adapting reinforcement learning methods [12,13], a robot can autonomously find the optimal behavior for finding a gas source through trial and error with the environment. A probabilistic method is another option as a learning method is data-hungry.…”

Section: Introductionmentioning

confidence: 99%

Integration of Bayesian Inference and Anemotaxis for Robotics Gas Source Localization in a Large Cluttered Outdoor Environment

et al. 2023

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Finding a gas source in a cluttered outdoor environment using autonomous robot is a complex challenge. The gas movement is difficult to predict as it is significantly affected by the wind and the shape of objects in the environment. In this paper, we propose a new probabilistic model and an integration of Bayesian inference and anemotaxis methods used for a robot to find a gas source in a large cluttered outdoor environment. An autonomous robot installed with a gas sensor is expected to find the location of the gas source after the gas leak occurs for a particular time. The advantage of the Bayesian inference technique has been presented previously so that a robot can find the gas source in an isolated indoor building without any significant wind flow. The large environment is divided into some particular regions. A set of probability density function was collected previously from a large amount of gas dispersion simulation to estimate the maximum likelihood of where the gas source is. The challenge gets more extensive if the Bayesian inference method is applied in an outdoor and cluttered environment. Instead of only measuring the gas concentration, the wind angle is also used as the wind profile significantly affects the gas dispersion. Therefore, the probability model is modified to allow the wind direction as a new variable. Moreover, an anemotaxis method is incorporated as the decision-making support as it may be more efficient to direct the robot explicitly to the upwind direction. Evaluations of the proposed method were carried out and its advantage was shown through simulation in a number of different scenarios.

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Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

Cited by 22 publications

References 31 publications

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

An Experimental Safety Response Mechanism for an Autonomous Moving Robot in a Smart Manufacturing Environment Using Q-Learning Algorithm and Speech Recognition

Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources Using Autonomous Surface Vehicles

Integration of Bayesian Inference and Anemotaxis for Robotics Gas Source Localization in a Large Cluttered Outdoor Environment

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