Gas distribution mapping

Gas distribution mapping (GDM) is the process of creating a representation of how gases spread in an environment from a set of spatially and temporally distributed measurements of relevant variables [9, 89]. Foremost, these measurements include the gas concentration itself, but may also comprise wind, pressure or temperature.

Gas distribution mapping is of great help not only because it can be used to pin- point the location of a gas source (or of multiple sources) without depending on the environmental conditions (see Section 2.4.1), but also because it provides informa- tion of how the gas emissions have spread in the environment, which is crucial in many real olfaction-related applications. For example, lets consider an industrial plant where a leak of a toxic gas has been detected. For safety considerations, it is not enough to locate the room or even the pipe that is leaking, but it is necessary to know which areas of the plant have been affected by the toxic gas to safely prepare the action plan.

Traditionally, the way to create a representation of the gas concentration field is to measure the response of a grid of gas sensors distributed in the environment [150, 71]. The main advantage of these networks of static gas sensors is that the instantaneous gas distribution can be obtained by reading all the sensors in the grid at a time, similar to taking a "picture" of how gases are distributed. Nonetheless, given the dynamic and changing characteristics of the gas distribution in real environments (see Section 2.3)

32

CHAPTER 2. ON TECHNOLOGY AND APPLICATIONS OF MOBILE ROBOTICS OLFACTION

for many applications it is better to obtain the time-averaged concentration field by averaging the readings over a prolonged time [66].

An important drawback of the sensor network is that it is not scalable when the area to inspect increases, rising considerably the deployment cost and reducing its flexibility. Because of this, the attention shifted to GDM with mobile robots, which using only one electronic nose allows obtaining gas maps with high flexibility. Ad- vantages of this approach include the use of only one sensing device (which may be complex and expensive), the capability of the robot to sample at adaptive reso- lutions depending on the area being inspected, and the possibility to use additional environmental information gathered by other sensors on board (cameras, laser scan- ners, anemometers, etc.) [70]. Chapter 5 discusses in detail the advantages of using mobile robots for GDM while providing an innovative approach which accounts for the obstacles in the environment and the dynamic characteristics of gas distribution.

Probably, the first work studying the distribution of gases with mobile robots was presented by Hayes et al. [56], where a group of mobile robots (swarm of robots) worked in a coordinated manner to create a histogram representation of the gas dis- tribution. The histogram bins contained the number of "odor hits", that is, the number of measurements above a predefined threshold. This binary information was collected by all the robots while inspecting the area following a simple random walk pattern. Apart from requiring an even coverage of the environment, this approach also takes a very long time to obtain statistically reliable data, and no extrapolation is performed to areas not inspected. These drawbacks lead to a bad scalability when applied to large environments, fact that makes doubtful its applicability in real scenarios.

Improvements to this approach were reported by Pyk et al. [121], employing bi- cubic interpolation to extrapolate the gas distribution to zones not directly inspected by the robot. A disadvantage of this method is, however, that no spatial averaging is carried out and therefore fluctuations appear directly on the map.

Nevertheless, the most remarkable works in this field have been reported by Lilienthal and colleagues. In the pioneer work [85] they proposed the kernel-based method, which consists of convolving sensor readings with a Gaussian kernel, thus providing a representation of the gas map without assuming any predefined paramet- ric form for the distribution. This method was later extended for the case of multiple odor sources [96] and to the three-dimensional case [124]. It was further shown how gas distribution mapping methods can be embedded into a Blackwellized particle fil- ter approach to account for the uncertainty about the position of the robot [87]. A deeper review of the works proposed in the field of GDM with ground mobile robots is later presented in Section 5.2.

In the last few years, it’s worth highlighting the attention paid by the research community to GDM with unmanned aerial vehicles (UAVs). Quickly deployable, cost-efficient or easy to transport are some of the advantages that flying mobile mea- surement devices provide when measuring the gas concentration outdoor [113]. Ad- ditionally, micro vertical take-off and landing (VTOL) UAVs, such as quadrocopters, have the ability to hover over a certain point of interest for a prolonged time, which makes them promising tools for environmental monitoring applications. For exam-

2.4. MOBILE ROBOTICS OLFACTION 33

ple, Kovacina et al. [76] proposed a decentralized control algorithm for localizing gas sources and mapping chemical clouds within a region. This approach relied on constrained randomized behaviors and attended to the UAV restrictions on sensors, computation, and flight envelope. Later, Bermúdez et al. [60] investigated the use of blimp-based gas-sensitive UAVs for demining tasks, including strategies for chemical mapping. Recent projects like AirShield (airborne remote sensing for hazard inspec- tion by network enabled lightweight drones) [15] investigate the use of autonomous swarm of micro UAVs to support emergency units. For a more detailed review of the state of the art in this field see [113].