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A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the EKF-LOAM, which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the EKF-LOAM uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the EKF-LOAM method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. Note to Practitioners—This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process. [ABSTRACT FROM AUTHOR]

One of the steps to provide fundamental data for planning a mining effort is the magnetic surveying of a target area, which is typically carried out by conventional aircraft campaigns. However, besides the high cost, fixed‐wing aerial vehicles present shortcomings especially for drape flights on mountainous regions, where steep slopes are often present. Traditional human‐crewed flights have to perform tedious and dangerous trajectories, under strict velocity and attitude constraints. In this paper, we deal with the problem of accomplishing digital magnetic‐elevation maps using autonomous and cooperative aerial robots. The proposed approach for autonomous mapping utilizes a custom‐built fluxgate sensor and off the shelf cameras adapted for small airborne platforms. We also propose an innovative approach for generating a digital magnetic‐elevation model from the gathered data. Our method was evaluated and validated in field tests in an industrial scenario to detect scrap metals in ore piles. Results show that the proposed method could reliably detect magnetic anomalies while generating accurate three‐dimensional magnetic maps. [ABSTRACT FROM AUTHOR]

SUMMARY: The field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting. [ABSTRACT FROM AUTHOR]

Recent advances in the research of autonomous vehicles have showed a vast range of applications, such as exploration, surveillance and environmental monitoring. Considering the mining industry, it is possible to use such vehicles in the prospection of minerals of commercial interest beneath the ground. However, tasks such as geophysical surveys are highly dependent on specific sensors, which mostly are not designed to be used in these new range of autonomous vehicles. In this work, we propose a novel magnetic survey pipeline that aims to increase versatility, speed and robustness by using autonomous rotary-wing Unmanned Aerial Vehicles (UAVs). We also discuss the development of a state-of-the-art three-axis fluxgate, where our goal in this work was to refine and adjust the sensor topology and coupled electronics specifically for this type of vehicle and application. The sensor was built with two ring-cores using a specially developed stress-annealed CoFeSiB amorphous ribbon, in order to get sufficient resolution to detect concentrations of small ferrous minerals. Finally, we report on the results of experiments performed with a real UAV in an outdoor environment, showing the efficacy of the methodology in detecting an artificial ferrous anomaly. [ABSTRACT FROM AUTHOR]

In this paper, we present a monocular vision-based height estimation algorithm for terrain following flights. The impressive growth of Unmanned Aerial Vehicle (UAV) usage, notably in mapping applications, will soon require the creation of new technologies to enable these systems to better perceive their surroundings. Specifically, we chose to tackle the terrain following problem, as it is still unresolved for consumer available systems. Virtually every mapping aircraft carries a camera; therefore, we chose to exploit this in order to use presently available hardware to extract the height information toward performing terrain following flights. The proposed methodology consists of using optical flow to track features from videos obtained by the UAV, as well as its motion information to estimate the flying height. To determine if the height estimation is reliable, we trained a decision tree that takes the optical flow information as input and classifies whether the output is trustworthy or not. The classifier achieved accuracies of 80% for positives and 90% for negatives, while the height estimation algorithm presented good accuracy. [ABSTRACT FROM AUTHOR]

This article presents perception and navigation systems for a family of autonomous orchard vehicles. The systems are customized to enable safe and reliable driving in modern planting environments. The perception system is based on a global positioning system (GPS)-free sensor suite composed of a twodimensional (2-D) laser scanner, wheel and steering encoders, and algorithms that process the sensor data and output the vehicle's location in the orchard and guidance commands for row following and turning. Localization is based on range data to premapped landmarks, currently one at the beginning and one at the end of each tree row. The navigation system takes as inputs the vehicle's current location and guidance commands, plans trajectories for row following and turning, and drives the motors to achieve fully autonomous block coverage. The navigation system also includes an obstacle detection subsystem that prevents the vehicle from colliding with people, trees, and bins. To date, the vehicles sporting the perception and navigation infrastructure have traversed over 350 km in research and commercial orchards and nurseries in several U.S. states. Time trials showed that the autonomous orchard vehicles enable efficiency gains of up to 58% for fruit production tasks conducted on the top part of trees when compared with the same task performed on ladders. Anecdotal evidence collected from growers and workers indicates that replacing ladders with autonomous vehicles will make orchard work safer and more comfortable. [ABSTRACT FROM AUTHOR]

Confined and subterranean areas are common in many civilian and industrial sites, although they are hazardous for humans given the presence of noxious gases, extreme temperatures, narrow spaces, unhealthy oxygen levels, flooding, and collapsing structures. Therefore, exploration, routine inspections, and surveillance tasks can benefit from using autonomous mobile robots to improve safety by reducing the presence of humans in those scenarios. However, despite advances in the field, there are still challenges to overcome for confined and subterranean robot operation. Real word robotic exploration requires robust and reliable map generation, precise localization, safe navigation, and efficient path planning. These requirements make exploration in complex 3D environments with rugged terrain difficult. The challenge is increased when considering multi-robot teams, as there is no guarantee of a functional network infrastructure. Despite consistent increasing interest in the area, there is a lack of research summarizing the results and best practices for exploring such environments. Therefore, in this paper, we provide a review and discuss state-of-the-art robotic exploration techniques, including single and cooperative approaches with homogeneous and heterogeneous teams, with a focus on complex subterranean and confined 3D scenarios. We also present a comprehensive list of insights on open challenges and possible directions for future investigation in the topic. • A survey in autonomous exploration for subterranean and confined spaces using mobile robots. • Description of current state of the art, including results from the DARPA subt Challenge. • Sensor classification for confined spaces inspection. • Comparison of recent works in autonomous exploration using multi-robot systems. [ABSTRACT FROM AUTHOR]

We apply a global sensitivity method, the Hilbert-Schmidt independence criterion (HSIC), to the reparametrization of a Zn/S/H ReaxFF force field to identify the most appropriate parameters for reparametrization. Parameter selection remains a challenge in this context, as high-dimensional optimizations are prone to overfitting and take a long time but selecting too few parameters leads to poor-quality force fields. We show that the HSIC correctly and quickly identifies the most sensitive parameters and that optimizations done using a small number of sensitive parameters outperform those done using a higher-dimensional reasonable-user parameter selection. Optimizations using only sensitive parameters (1) converge faster, (2) have loss values comparable to those found with the naive selection, (3) have similar accuracy in validation tests, and (4) do not suffer from problems of overfitting. We demonstrate that an HSIC global sensitivity is a cheap optimization preprocessing step that has both qualitative and quantitative benefits which can substantially simplify and speed up ReaxFF reparametrizations.

In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager., (© 2022. The Author(s).)