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Faezeh Rahbar
Publications associées (10)
Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots
Chiara Ercolani, Wanting Jin, Alcherio Martinoli, Faezeh Rahbar
The deployment of robots for Gas Source Lo- calization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersion, with a current trend towards tackling more complex environments. However, most state-of-art GSL algorithms for environments with obstacles only depend on local information, leading to low efficiency in large and more structured spaces. The efficiency of GSL can be improved dramatically by coupling it with a global knowledge of gas distribution in the environment. However, since gas dispersion in a built environment is difficult to model analytically, most previous work incorporating a gas dispersion model was tested under simplified assumptions, which do not take into consideration the impact of the presence of obstacles to the airflow and gas plume. In this paper, we propose a probabilistic algorithm that enables a robot to efficiently localize gas sources in built environments, by combining a state-of-the-art probabilistic GSL algorithm, Source Term Estimation (STE) with a learned plume model. The pipeline of generating gas dispersion datasets from realistic simulations, the training and validation of the model, as well as the integration of the learned model with the STE framework are presented. The performance of the algorithm is validated both in high-fidelity simulations and real experiments, with promising results obtained under various obstacle configurations.
2023Source Term Estimation Algorithms for Gas Sensing Mobile Robots
Localizing sources of airborne chemicals with mobile sensing systems finds applications in various crucial and perilous situations, such as safety and security investigation for detecting explosives or illegal drugs, search and rescue operations to locate survivors in the aftermath of natural hazards, or environmental monitoring in unsafe sites, following harmful leaks. Using autonomous robots in such situations would eliminate or, at least, reduce human intervention and keep them from harm. Additionally, such operations would be more cost-effective and more time-efficient. That is why, in the past 30 years, gas source localization has been an attractive research topic in robotics and related areas, where different methods have been designed and evaluated for this purpose.However, the inherent complexity of gas dispersal phenomena, which is non-trivial to analyze and predict, especially in complex environments, is the main source of challenges in this field. Therefore, researchers tend to design and evaluate algorithms in simplistic environments before tackling more complex ones.In this thesis, we have designed and investigated a gas source localization algorithm based on source term estimation with a probabilistic approach. After validating the performance of the method in a baseline environment using a wheeled-robot, we gradually enhanced our method by enriching it with new features in order to be adaptable to more complex scenarios.In particular, the algorithm was shown to be successful in a simplified three-dimensional setup as well as in an unknown environment where no global map and positioning system is available. Furthermore, it was deployed on a homogeneous multi-robot system, where different coordination strategies between robots were designed and studied. Finally, designing a data-driven plume model and integrating it to the main framework of the method allowed for adaptation to cluttered environments. The method is systematically evaluated through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions. Lastly, the performance of our algorithm was compared with other state-of-the art methods to show its potentials and limits.
EPFL2021A Distributed Source Term Estimation Algorithm for Multi-Robot Systems
Alcherio Martinoli, Faezeh Rahbar
Finding sources of airborne chemicals with mobile sensing systems finds applications in safety, security, and emergency situations related to medical, domestic, and environmental domains. Given the often critical nature of all the applications, it is important to reduce the amount of time necessary to accomplish this task through intelligent systems and algorithms. In this paper, we extend a previously presented algorithm based on source term estimation for odor source localization for homogeneous multi-robot systems. By gradually increasing the level of coordination among multiple mobile robots, we study the benefits of a distributed system on reducing the amount of time and resources necessary to achieve the task at hand. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions in different coordination scenarios and with different number of robots.
2020An Algorithm for Odor Source Localization based on Source Term Estimation
Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
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.
2019An Algorithm for Odor Source Localization based on Source Term Estimation
Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
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.
IEEE2019Design and Performance Evaluation of an Algorithm Based on Source Term Estimation for Odor Source Localization
Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical 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 a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.
2019Design and Performance Evaluation of an Infotaxis-Based Three-Dimensional Algorithm for Odor Source Localization
Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar, Julian Ruddick
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.
IEEE2018A 3-D Bio-inspired Odor Source Localization and its Validation in Realistic Environmental Conditions
Pierre Charles Marie Kibleur, Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
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 Levy 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.
2017Adaptive Lévy Taxis for Odor Source Localization in Realistic Environmental Conditions
Romain Jean-Paul Emery, Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
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.
2017An Algorithm for Odor Source Localization based on Source Term Estimation
Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar
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.
IEEE