UAV Path Planning and Multi-Modal Localization for Mapping in a Subterranean Environment

The use of Unmanned Aerial Vehicles (UAVs), especially quadcopters, have become popular in academia and industry due to their small size and maneuverability. These UAVs can be programmed to autonomously execute missions that are usually difficult and risky for humans, such as subterranean exploration, infrastructure surveying, and even disaster response. However, inaccessible and remote environments pose a challenge in terms of navigation as they often lack access to Global Navigation Satellite System (GNSS) connections and lack features. To address these challenges, UAVs are equipped with multiple sensors to acquire different types of data. These include range, acceleration, and even images, which are fused to estimate a localization solution. The Autonomous Robotic Early Warning System for Underground Stone Mining Safety project, sponsored by Alpha Foundation, is conducted within the Statler College at West Virginia University (WVU). The project aims to map walls and pillars within a mine to analyze its structural integrity and safety. This thesis investigates the implementation of a path planning strategy to optimize the coverage of a wall and also the use of an error state Extended Kalman Filter (EKF) for sensor fusion to perform Simultaneous Localization and Mapping (SLAM). The experiments were carried out both in simulated and real world environments. In these experiments, the UAV was equipped with an Inertial Measurement Unit (IMU), laser altimeter, Ultra-Wideband (UWB) module (for ranging data), LiDAR for mapping, and an RGB-D camera to provide a Visual Odometry (VO) solution. In the simulation, the 3D reconstruction and odometry was compared to the ground truth, whereas the real experiment provided further insight into the strategy’s feasibility

Introducing Intelligence and Autonomy into Industrial Robots to Address Operations into Dangerous Area

The paper addresses the issue to use new generation robotic systems inside industrial facilities in order to complete operations in dangerous area. The new robotic systems are currently adopting the autonomous approach already in use in military sector; however, in this context the intensity of operations and the necessity to interact with high productivity systems introduce different challenges. Despite the problems, it is evident that this approach could provide very interesting improvements in terms of safety for humans especially in relations to dangerous area. For instance, in confined spaces, Oil & Gas or Hot Metal Industry these new autonomous systems could reduce the number of injures and casualties. In addition, these systems could increase the operation efficiency in this complex frameworks as well as the possibility to carry out inspections systematically; in this sense, this could result in improving the overall reliability, productivity and safety of the whole Industrial Plant. Therefore, it is important to consider that these systems could be used to address also security aspects such as access control, however they could result vulnerable to new threats such as the cyber ones and need to be properly designed in terms of single entities, algorithms, infrastructure and architecture. From this point of view, it is evident that Modeling and Simulation represent the main approach to design properly these new systems. In this paper, the authors present the use of autonomous systems introducing advanced capabilities supported by Artificial Intelligence to deal with complex operations in dangerous industrial frameworks. The proposed examples in oil and gas and hot metal industry confirm the potential of these systems and demonstrate as simulation supports their introduction in terms of engineering, testing, installation, ramp up and training

Collaborative Human-Robot Exploration via Implicit Coordination

This paper develops a methodology for collaborative human-robot exploration that leverages implicit coordination. Most autonomous single- and multi-robot exploration systems require a remote operator to provide explicit guidance to the robotic team. Few works consider how to embed the human partner alongside robots to provide guidance in the field. A remaining challenge for collaborative human-robot exploration is efficient communication of goals from the human to the robot. In this paper we develop a methodology that implicitly communicates a region of interest from a helmet-mounted depth camera on the human's head to the robot and an information gain-based exploration objective that biases motion planning within the viewpoint provided by the human. The result is an aerial system that safely accesses regions of interest that may not be immediately viewable or reachable by the human. The approach is evaluated in simulation and with hardware experiments in a motion capture arena. Videos of the simulation and hardware experiments are available at: https://youtu.be/7jgkBpVFIoE.Comment: 7 pages, 10 figures, to appear in the 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR

Present and Future of SLAM in Extreme Underground Environments

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE Transactions on Robotics for pre-approva

Hierarchical Collision Avoidance for Adaptive-Speed Multirotor Teleoperation

This paper improves safe motion primitives-based teleoperation of a multirotor by developing a hierarchical collision avoidance method that modulates maximum speed based on environment complexity and perceptual constraints. Safe speed modulation is challenging in environments that exhibit varying clutter. Existing methods fix maximum speed and map resolution, which prevents vehicles from accessing tight spaces and places the cognitive load for changing speed on the operator. We address these gaps by proposing a high-rate (10 Hz) teleoperation approach that modulates the maximum vehicle speed through hierarchical collision checking. The hierarchical collision checker simultaneously adapts the local map's voxel size and maximum vehicle speed to ensure motion planning safety. The proposed methodology is evaluated in simulation and real-world experiments and compared to a non-adaptive motion primitives-based teleoperation approach. The results demonstrate the advantages of the proposed teleoperation approach both in time taken and the ability to complete the task without requiring the user to specify a maximum vehicle speed.Comment: 8 pages, 8 figures, to be published in the 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR

Development of a ground robot for indoor SLAM using Low‐Cost LiDAR and remote LabVIEW HMI

The simultaneous localization and mapping problem (SLAM) is crucial to autonomous navigation and robot mapping. The main purpose of this thesis is to develop a ground robot that implements SLAM to test the performance of the low‐cost RPLiDAR A1M8 by DFRobot. The HectorSLAM package, available in ROS was used with a Raspberry Pi to implement SLAM and build maps. These maps are sent to a remote desktop via TCP/IP communication to be displayed on a LabVIEW HMI where the user can also control robot. The LabVIEW HMI and the project in its entirety is intended to be as easy to use as possible to the layman, with many processes being automated to make this possible. The quality of the maps created by HectorSLAM and the RPLiDAR were evaluated both qualitatively and quanitatively to determine how useful the low‐cost LiDAR can be for this application. It is hoped that the apparatus developed in this project will be used with drones in the future for 3D mapping