Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multiagent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a lightweight collision avoidance; (iii) how to learn the data's model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data's model online while exploring.
PreprintThis is the submitted version of a paper published in Robotics and Autonomous Systems.Citation for the original published paper (version of record):Wiedemann, T., Shutin, D., Lilienthal, A J. (2019) Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties Robotics and Autonomous Systems, Abstract Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multirobot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurements location and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed a experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.
In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.