Chiara Ercolani

Related publications (6)

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

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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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

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

3D Odor Source Localization Using a Micro Aerial Vehicle: System Design and Performance Evaluation

Chiara Ercolani, Alcherio Martinoli

2020

Steerable Locomotion Controller for Six-strut Icosahedral Tensegrity Robots

Chiara Ercolani, Massimo Vespignani

This paper proposes a novel steerable locomotion controller for six-strut tensegrity robots. Tensegrity robots are lightweight and have many promising features such as robustness, shape-shifting capabilities, and deployability, making them good candidates for exploration and scouting of remote areas. Despite these advantages, tensegrity robots are challenging to control due to their large number of degrees of freedom, nonlinear dynamics, and intrinsic compliance. Recently, many step-wise motion controllers have been employed to simplify the locomotion problem, thanks to the discrete nature of the tensegrity structure. In this paper we present a novel locomotion controller which will steer the direction of motion of a six-strut tensegrity robot when used in conjunction with any preexisting step-wise controller. We validated our controller on the SUPERball v2 robot, showing straight and curved trajectories, and an example of navigation around obstacles. Our method is computationally inexpensive, only requires knowledge about the current base triangle (e.g, via accelerometer data), and can be generalized to any six-strut tensegrity robot which can perform step-wise locomotion.

2018