Finding sources of airborne chemicals with mobile sensing systems finds applications across the security, safety, domestic, medical, and environmental domains. In this paper, we present an algorithm based on source term estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose an innovative strategy to balance exploration and exploitation in navigation. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments.

In this paper we tackle the problem of finding the source of a gaseous leak with a robot in a three-dimensional (3-D) physical space. The proposed method extends the operational range of the probabilistic Infotaxis algorithm [1] into 3-D and makes multiple improvements in order to increase its performance in such settings. The method has been tested systematically through high-fidelity simulations and in a wind tunnel emulating realistic conditions. The impact of multiple algorithmic and environmental parameters has been studied in the experiments. The algorithm shows good performance in various environmental conditions, particularly in high wind speeds and different source release rates.

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three moth-inspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate.

Finding the source of gaseous compounds released in the air with robots finds several applications in various critical situations, such as search and rescue. While the distribution of gas in the air is inherently a 3D phenomenon, most of the previous works have downgraded the problem into 2D search, using only ground robots. In this paper, we have designed a bio-inspired 3D algorithm involving cross-wind Lévy Walk, spiralling and upwind surge. The algorithm has been validated using high-fidelity simulations, and evaluated in a wind tunnel which represents a realistic controlled environment, under different conditions in terms of wind speed, source release rates and odor threshold. Studying success rate and execution time, the results show that the proposed method outperforms its 2D counterpart and is robust to the various setup conditions, especially to the source release rate and the odor threshold.

The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors - upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble's stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy.

This paper tackles the problem of multi-robot odor distribution mapping through time series analysis. Considering the conditions of real world environments where the chemical concentration distribution is patchy, intermittent and time-variant, we propose a method to incorporate the temporal and spatial aspect of sensory data into the problem of odor distribution mapping. Despite the previous works in this field, the method gives more importance to the recent acquired measurements and also to the measurements which have been spatially closer to the place of the sensors (at the time of their acquisition). Real experiments were done in a realistic small-scale controlled environment (designed for systematic olfactory tests), considering up to five real robots and two different navigation algorithms. Experiments show that the generated odor maps are remarkably more accurate than the results of the conventional spatial interpolation method. Studying various spatio-temporal neighborhoods in the time series analysis concluded that a proper definition of the neighborhood (in time and space) provides accurate results in gas distribution mapping.

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

This paper presents a novel robot swarming navigation algorithm in order to find the odor sources in an unknown environment, based on the ability of each swarm member to sense the odor. Each robot in the swarm has a cooperative localization system which uses wireless network as a mean of measuring the distance from the other robots. In this method, at least three robots act as stationary measurement beacons while the other robots of the swarm navigate in the environment towards the odor source. In the next step, the roles of the robots will be switched and some other robots will act as beacons. The experimental tests report a good result in finding the odor source and also the accuracy of localization system.

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements.

3DCLIMBER is a running project in the University of Coimbra for developing a climbing robot with the capability of manipulating over 3D human-made structures. This paper mainly discuss the conceptual and detailed design and development of a Pole Climbing robot with minimum degrees of freedom which can climb over 3D structures with bends and branches followed by Preliminary test results of the robot performance. Electronics architecture and control algorithms are briefly described. The paper finishes with discussion of the current results and identifies some future works.