Alcherio Martinoli

Biographie

I received my Diploma in Electrical Engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ), and a Ph.D. in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). I am currently an Associate Professor at the School of Architecture, Civil, and Environmental Engineering and the head of the Distributed Intelligent Systems and Algorithms Laboratory. Before joining EPFL I carried out research activities at the Institute of Biomedical Engineering of the ETHZ, at the Institute of Industrial Automation of the Spanish Research Council in Madrid, Spain, and at the California Institute of Technology, Pasadena, U.S.A. Additional information can be found on my full CV.

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https://people.epfl.ch/alcherio.martinoli?lang=fr

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Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods.

2023

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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.

2023

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A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Cyrill Silvan Baumann, Alcherio Martinoli

The automatic design of well-performing robotic controllers is still an unsolved problem due to the inherently large parameter space and noisy, often hard-to-define performance metrics, especially when sequential tasks need to be accomplished. Distal control architectures, which combine precoded basic behaviors into a (probabilistic) finite state machine offer a promising solution to this problem. In this paper, we enhance a Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm with an Optimal Computing Budget Allocation (OCBA) scheme to automatically synthesize distal control architectures. We benchmark MDPSO-OCBA’s performance against the original MDPSO as well as the Iterated F-Race (IRACE) and the Mesh Adaptive Direct Search (MADS) algorithms on both a benchmark function with different noise levels and design problems of distal control architectures. More specifically, we evaluate the algorithms using high-fidelity simulations in three increasingly challenging scenarios involving parallel and sequential tasks. Additionally, the best performing controller generated in simulation by each optimization algorithm is compared with a manually designed solution and validated with physical experiments. The analysis on the benchmark function with different noise levels demonstrates MDPSO-OCBA’s high robustness to noise. The comparison on the robotic control design problems shows that, without any meta-parameter tuning, MDPSO-OCBA is able to generate the best performing control architectures overall, closely followed by IRACE. They significantly outperform MADS for the more complex and noisier scenarios, resulting in competitive controllers in comparison to the manually designed one.

2022

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