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Alcherio Martinoli

Publications associées (269)

Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge

Chiara Ercolani, Alcherio Martinoli, Thomas Laurent Peeters

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

Chargement

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

Chargement

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

Chargement

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Chemical gas dispersion can represent a severe threat to human and animal lives, as well as to the environment. Constructing a map of the distribution of gas in a fast and reliable manner is critical to ensure accurate monitoring of at-risk facilities and coordinate targeted and swift rescue missions when an emergency occurs. In recent years, robots have been endowed with gas sensing capabilities, and several algorithms to generate gas maps have been studied, often producing a 2D map. Two of the major drawbacks of these studies are concerned with the fact that the robot’s path is often fixed to a predefined route, and that the tridimensionality of the gas dispersion phenomenon is not captured by the final gas maps. In this letter, we study the effect of a random walk and an adaptive path planning approach based on informative quantities to gas mapping in a 3D environment using a micro aerial vehicle with severe flight time constraints. We also introduce a clustering strategy to enhance the exploration of the environment. The strategies are compared to a lawnmower movement and evaluated against a ground truth map of the environment, both in simulation and with physical experiments, in order to assess which ones are able to provide an accurate gas map, while simultaneously achieving satisfactory coverage of the desired volume under time constraints.

2022

Chargement

GaSLAM: An Algorithm for Simultaneous Gas Source Localization and Gas Distribution Mapping in 3D

Chiara Ercolani, Alcherio Martinoli, Lixuan Tang

Chemical gas dispersion poses considerable threat to humans, animals and the environment. The research areas of gas source localization and gas distribution mapping aim to localize the source of gas leaks and map the gas plume respectively, in order to help the coordination of swift rescue missions. Although very similar, these two areas are often treated separately in literature. In some cases, inferences on the gas distribution are made a posteriori from the source location, or vice-versa. In this paper, we introduce GaSLAM, a methodology that couples the estimation of the gas map and the source location using two state of the art algorithms with a novel navigation strategy based on informative quantities. The synergistic approach allows our algorithm to achieve a good estimation of both objectives and push the navigation strategies towards informative areas of the experimental volume. We validate the algorithm in simulation and with physical experiments in varying environmental conditions. We show that the algorithm improves on the source location estimate compared to a similar approach found in literature, and is able to deliver good quality maps of the gas distribution.

2022

Chargement

Hybrid Flock - Formation Control Algorithms

Cyrill Silvan Baumann, Alcherio Martinoli, Jonas Perolini, Emna Tourki

Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typically, they are treated separately, we believe that a combination of flock and formation control represents a promising algorithmic solution. Such an algorithm could leverage a combination of characteristics of both categories best suited for a given situation. In this work, we therefore propose two distributed algorithms, able to gradually and reversibly shift between flocking and formation behaviors using a single parameter W. We evaluate them using both simulated and real robots with and without the presence of obstacles.We find that both algorithms successfully trade off flock density for formation error. Furthermore, using a funnel experiment as application case study, we demonstrate that an adaptive shift between flock and formation behavior, using a simple method to define W in real-time using exclusively on-board resources, results in a statistically relevant reduction of traversing time in comparison to a non-adaptive formation control algorithm.

2022

Chargement

Leveraging Multi-Level Modelling to Automatically Design Behavioral Arbitrators in Robotic Controllers

Cyrill Silvan Baumann, Hugo Richard Birch, Alcherio Martinoli

Automatic control design for robotic systems is becoming more and more popular. However, this usually involves a significant computational cost, due to the expensive and noisy evaluation of candidate solutions through highfidelity simulation or even real hardware. This work aims at reducing the computational cost of automatic design of behavioral arbitrators through the introduction of a two-step approach. In the first step, the structure of the finite state machine governing the behavioral arbitrator is optimized. To this purpose, a more abstracted model of the robotic system is leveraged in order to significantly reduce the computational cost. In the second step, the close-to-hardware, behavioral parameters are fine-tuned using a high-fidelity model. We show that, for a scenario involving a single robot and multiple tasks to be solved sequentially, using the proposed method results in a significant decrease of the computational cost while reaching the same controller performance both in simulation and reality.

2022

Chargement

In this work, we address the design of tightly integrated control, estimation, and allocation algorithms allowing a group of robots to move collectively. For doing so, we leverage a modular framework that allows us to define precisely the needed functional components and thus consider and compare multiple algorithmic solutions for the same module. We demonstrate the effectiveness of such a framework through multiple spatial coordination challenges carried out both in simulation and reality and leveraging different distributed control laws (graph-based and behavior-based controllers). Moreover, we investigate the impact of different localization and communication constraints as well as that of real-time switching of control laws on selected coordination metrics. Finally, we also introduce additional algorithmic components for demonstrating further the modularity of the framework. We find that defining the modularity based on functionality is a very effective way to enable algorithm benchmarking and discover possible improvements of the overall software stack while at the same time being agnostic to the underlying hardware and middleware resources. This is an especially welcome feature in case of severely resource-constrained multi-robot systems. Moreover, an important benefit of such design process is that the resulting distributed control algorithms are very robust to the considered noise sources and amplitudes as well as to the diverse types of challenges considered.

2021

Chargement

Accurate localization is one of the biggest challenges in underwater robotics. The primary reasons behind that are unavailability of satellite-based positioning below the surface, and lack of clear features in natural water bodies for visually aided localization. As such, the common method of choice for external position referencing in underwater robots is the use of acoustic signals for computing range or direction of arrival. To that end, we have developed an acoustic range based navigation system with floating, movable beacons. In this paper, we present an approach for planning the trajectory of acoustic beacons in a way that they provide the best possible navigation support for a group of underwater vehicles. We use an information theoretic approach to beacon path planning that minimizes the group’s position uncertainty. We evaluate our approach with realistic simulations calibrated using real-world data, and present results.

2021

Chargement

Distributed Cooperative Localization with Efficient Pairwise Range Measurements

Alcherio Martinoli, Anwar Ahmad Quraishi

We present a method based on covariance intersection for cooperative localization with pairwise range-only relative measurements. Our method was designed for underwater robots equipped with an acoustic communication and ranging system. Range measurements are not sufficient to compute a complete relative

3

D position. Therefore, covariance intersection is performed in a transformed space along their relative estimated positions, while preserving cross-correlations between other state variables. Given the characteristics of the acoustic channel, only one robot can transmit data or a ranging request at a time, hence the pairwise limitation. We also present a heuristic for choosing a peer robot for a range measurement by maximizing mutual information. Our method places no further restrictions on the order, timing or scheduling of relative measurements. We evaluated our method for accuracy and consistency, and present results from simulations as well as outdoor experiments.

2021

Chargement

Distributed Cooperative Localization with Efficient Pairwise Range Measurements

Alcherio Martinoli, Anwar Ahmad Quraishi

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2021