Olfaction, Vision, and Semantics for Mobile Robots. Results of the IRO Project

Monroy, Javier; Ruiz-Sarmiento, José-Raúl; Moreno, Francisco-Ángel; Galindo, Cipriano; González-Jiménez, Javier

doi:10.3390/s19163488

Cited by 6 publications

(2 citation statements)

References 52 publications

(63 reference statements)

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“…By combining data on a Support Vector Machine (SVM), the system is able to differentiate different types of rooms. A recent work exploits other kind of environmental data, combining information coming from an artificial nose with visual information [47]. By doing so, the system is able to detect objects that emit certain kinds of gas.…”

Section: Understanding the Environmentmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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Section: Understanding the Environmentmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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“…However, as the development of olfaction-based techniques advances and evolves, new requirements arise for these tools. A prime example of this is how, in recent years, research into solutions that combine olfaction with other types of sensing (mostly vision) has begun to be carried out [ 19 , 20 , 21 , 22 ]. Thus, the development of simulation tools that allow for a proper integration of different sensing systems becomes necessary.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots

Ojeda

Monroy

González-Jiménez

2021

Sensors

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The simulation of how a gas disperses in a environment is a necessary asset for the development of olfaction-based autonomous agents. A variety of simulators already exist for this purpose, but none of them allows for a sufficiently convenient integration with other types of sensing (such as vision), which hinders the development of advanced, multi-sensor olfactory robotics applications. In this work, we present a framework for the simulation of gas dispersal and sensing alongside vision by integrating GADEN, a state-of-the-art Gas Dispersion Simulator, with the Unity 3D, a video game development engine that is used in many different areas of research and helps with the creation of visually realistic, complex environments. We discuss the motivation for the development of this tool, describe its characteristics, and present some potential use cases that are based on cutting-edge research in the field of olfactory robotics.

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