This paper deals with the design of an electronic device aimed at the detection and characterization of volatile chemical substances, that is, an electronic nose (e-nose). We pursue the development of a versatile, multipurpose e-nose that can be employed for a wide variety of applications, can integrate heterogeneous sensing technologies, and can offer a mechanism to be customized for different requirements. To that end, we contribute with a fully configurable and decentralized e-nose architecture based on self-contained and intelligent sensor boards (i.e., modules). This design allows for the integration not only of heterogeneous gas sensor technologies, like MOX and AEC sensors, but also of other components, such as GPS or Bluetooth, for a total of up to 127 individual modules. We describe an implementation of a fully operative prototype as an illustrative example of its potential for sensor networks, mobile robotics, and wearable technologies, each using different combinations of sensors.

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.

Multicopters are lightweight and maneuverable aerial vehicles yet unable to carry heavy payloads, such as large sensors or computers required for indoor autonomous navigation. Therefore, localization is usually performed by using vision-based solutions employing of either lightweight on-board cameras or external fixed cameras and a ground station for data-processing. Nevertheless, the current tendency is to use a low-power on-board computers to perform all computation on the multicopter itself. This paper covers the enhancement of a commercial multicopter, also called drone, with computation ability and sensorial devices for autonomous flight without the need of a ground-station. We describe the hardware and software integrated into the drone, which will be used for the future development of 6DoF navigation algorithms. The resulting system is able to work with most standard sensors and has the possibility to change them as needed. Also, we demonstrate the correct behavior of the drone by using a test navigation program that autonomously follows a moving beacon at constant distance and controlled altitude using an RGB-D camera and a sonar.

This paper describes an experiment for gas-source localization with a human-teleoperated mobile robot devised to gather data on how humans search for odor-sources. To that end, more than 150 repetitions of the search process are recorded for 69 test subjects, under 4 sensor configurations (including electronic nose, anemometer and video camera) and 4 scenarios (i.e. with different wind-flow conditions and gas-source position). The experiment has been carried out with a ROS-based simulator that allows driving the robot while recording data of interest (e.g. driving commands, robot localization, sensor measurements, ground-truth, etc.) for further analyzing the human process of gas-source searching, and computational fluid dynamics (CFD) to generate realistic and repeatable test conditions. The manuscript describes the different environmental parameters and sensor combinations of the experiment, and explains the methodology under which it was executed. The obtained dataset is publicly available at http://mapir.isa.uma.es/mapirwebsite/index.php/253-gsl-dataset.

Gas source localization (GSL) is one of the most important and direct applications of a gas sensitive mobile robot, and consists in searching for one or multiple volatile emission sources with a mobile robot that has improved sensing capabilities (i.e. olfaction, wind flow, etc.). This work adresses GSL by employing a teleoperated mobile robot, and focuses on which search strategy is the most suitable for this teleoperated approach. Four different search strategies, namely chemotaxis, anemotaxis, gas-mapping, and visual-aided search, are analyzed and evaluated according to a set of proposed indicators (e.g. accuracy, efficiency, success rate, etc.) to determine the most suitable one for a human-teleoperated mobile robot. Experimental validation is carried out employing a large dataset composed of over 150 trials where volunteer operators had to locate a gas-leak in a virtual environment under various and realistic environmental conditions (i.e. different wind flow patterns and gas source locations). We report different findings, from which we highlight that, against intuition, visual-aided search is not always the best strategy, but depends on the environmental conditions and the operator's ability to understand how gas distributes.