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.

The deployment of robots for Gas Source Localization (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.

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.

Finding chemical compounds in the air has applications when situations such as gas leaks, environmental emergencies and toxic chemical dispersion occur. Enabling robots to undertake this task would provide a powerful tool to prevent dangerous situations and assist humans when emergencies arise. While the dispersion of chemical compounds in the air is intrinsically a three-dimensional (3D) phenomenon, the scientific community tackled primarily two-dimensional (2D) scenarios so far. This is mainly due to the challenges of developing a platform able to successfully provide chemical compounds samples of a 3D space. In this paper, a 3D bioinspired algorithm for odor source localization, previously validated in a controlled physical environment leveraging a robotic manipulator, is adapted for deployment on a micro aerial vehicle equipped with an odor sensor. Given the effect that the propellers have on a gas distribution, the algorithmic adaptation focused on enhancing the sensing strategy of the platform. Additionally, two sensor placement configurations are assessed to determine which one yields best sensing results. A performance evaluation in different environmental scenarios is carried out to test the robustness of the implementation. Two different localization systems are used for the performance evaluation experiments to quantify the impact of localization accuracy on the algorithm's outcome.

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.