Gas source localization in complex environments is critical for applications such as environmental monitoring, industrial safety, and disaster response. Traditional methods often struggle with the challenges posed by a lack of environmental topography integration, especially when interactions between wind and obstacles distort gas dispersion patterns. In this paper, we propose a deep learning-based approach, which leverages spatial context and environmental mapping to enhance gas source localization. By integrating Simultaneous Localization and Mapping (SLAM) with a U-Net-based model, our method predicts the likelihood of gas source locations by analyzing gas sensor data, wind flow, and topography of the environment represented by a 2D occupancy map. We demonstrate the efficacy of our approach using a wheeled robot equipped with a photoionization detector, a LIDAR, and an anemometer, in various scenarios with dynamic wind fields and multiple obstacles. The results show that our approach can robustly locate gas sources, even in challenging environments with fluctuating wind directions, outperforming conventional methods by utilizing topography contextual information. This study underscores the importance of topographical context in gas source localization and offers a flexible and robust solution for real-world applications. Data and code are publicly available.

A swarm of robots offers significant advantages over a single robot, enabling faster exploration of larger areas and enhanced robustness against single-point failures. Accurate relative positioning is critical for executing collaborative missions without collisions. When Visual Simultaneous Localization and Mapping (VSLAM) is employed to estimate each robot’s poses, the inter-agent loop closing method is commonly used to improve relative positioning accuracy by refining pose estimates and merging local maps based on shared feature points. However, this approach demands considerable computational and communication resources. In this paper, we introduceCollaborativeSLAMusingVisualOdometry andRanges (CoVOR-SLAM) to address these challenges. CoVOR-SLAM significantly reduces both the amount of data transmitted between robots and their computational loads, as it requires only pose estimates, covariances, and range measurements instead of feature or map points for inter-agent loop detection. The necessary range measurements can be derived from pilot signals within the communication system, eliminating the need for complex additional infrastructure. We evaluated CoVOR-SLAM using real images and ultra-wideband range data collected from two rovers, as well as in a larger multi-agent setup utilizing public image datasets and realistic simulations. The results demonstrate that CoVOR-SLAM accurately estimates robot poses while requiring considerably less computational power and communication capacity than inter-agent loop closing techniques.

In this paper, we present a complete analytic probability based description of mobile-to-mobile uncorrelated scatter channels. We provide a theoretical proof that the proposed probability based description is equivalent to the correlation based description introduced by Bello and Matz. This equivalence is evaluated through a comparison of the hybrid characteristic probability density function with the correlation based description of a measured generic mobile-to-mobile channel, both of which can be obtained directly either from theory or from measurement data. The comparison confirms the similarity between the probability based and correlation based description qualitatively and quantitatively. Thus, the proposed probabilistic description complements the common correlation based description providing a comprehensive theoretical description of arbitrary uncorrelated scatter channels.

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite Systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér-Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars.

Non-stationary channel models play a crucial role in today's communication systems. Mobile-to-mobile channels are known to exhibit non-stationary behavior caused by the movement of transmitter and receiver. Non-stationarity can be addressed by introducing time-variant stochastic functions such as the time-variant instantaneous Doppler probability density function or time-variant instantaneous characteristic function. An algebraic analysis of the time-variant Doppler probability density function (pdf) in a classical Cartesian coordinate system is only numerically tractable due to trigonometric functions in the resulting expressions. In contrast, it has been shown that by using prolate spheroidal coordinates for 2D vehicle-to-vehicle channels the algebraic analysis becomes analytically tractable. In this paper, the analysis is extended to air-to-air channels. It is shown that the time-variant Doppler pdf can be represented without trigonometric functions. The description of the Doppler frequency in prolate spheroidal coordinates allows describing it as an algebraic curve. This permits the use of algebraic methods to analyze the Doppler frequency and derive the boundaries of the resulting Doppler pdf. Using the developed tools, we have investigated exemplary air-to-air scenarios. However, the methodology can be extended to any air-to-air configuration.

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, multi-agent 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 light-weight 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.