This chapter presents the application of autonomous Aerial Robotic Workers towards performing a visual inspection of 3D infrastructures by utilizing single and multiple Aerial Robotic Workers (ARWs). To address this problem, the developed framework combines the fundamental tasks of path planning, localization, and mapping, which are the essential components for autonomous robotic navigation systems. In the presented approach, the Unmanned Aerial Workers (ARWs) deployed for inspecting the structure rely only on their onboard computer and sensory system. Initially, the problem of path planner is discussed and mathematically formulated, leading to the development of a geometry-based approach for coverage of complex structures. The navigation of the platform is performed through the localization component, which provides accurate pose estimation for the vehicle using a visual-inertial estimation scheme. During the coverage mission, the agents collect image data for post-processing and mapping using Visual SLAM and Structure from Motion techniques. The performance of the proposed framework has been experimentally evaluated in multiple indoor and realistic outdoor infrastructure inspection experiments, depicting the merits of the autonomous navigation system (path planning and localization) and 3D model building of the inspected object and infrastructure.

This chapter will present the application of deployment of full autonomous Aerial Robotic Workers for inspection and exploration tasks in Subterranean environments. The framework shown will focus on the navigation, control, and perception capabilities of resource-constrained aerial platforms, contributing to the development of consumable scouting robotic platforms for real-life applications in extreme environments. In the approach, the aerial platform will be treated as a floating object, commanded by a velocity controller on the x-y axes, a height controller, as well as a heading correction module aligning the platform with the mining tunnel open space. Multiple experimentally verified methods regarding the heading correction module, for dark environments with limited texture, using either a visual camera or a 2D LiDAR presented in real mining environments are presented.

Navigation in underground mine environments is a challenging area for the aerial robotic workers. Mines usually have complex geometries, including multiple crossings with different tunnels. Moreover, improving the safety of mines requires drones to be able to detect human workers. Thus, in this Chapter, we introduce frameworks for junction and human detection in the underground mine environments.

This work establishes COMPRA, a compact and reactive autonomy framework for fast deployment of Micro Aerial Vehicles (MAVs) in subterranean Search-and- Rescue (SAR) missions. A COMPRA-enabled MAV is able to autonomously explore previously unknown areas while specific mission criteria are considered e.g. an object of interest is identified and localized, the remaining useful battery life, the overall desired exploration mission duration. The proposed architecture follows a low-complexity algorithmic design to facilitate fully on-board computations, including nonlinear control, state-estimation, navigation, exploration behavior and object localization capabilities. The framework is mainly structured around a reactive local avoidance planner, based on enhanced Potential Field concepts and using instantaneous 3D pointclouds, as well as a computationally efficient heading regulation technique, based on depth images from an instantaneous camera stream. Those techniques decouple the collision-free path generation from the dependency of a global map and are capable of handling imprecise localization occasions. Field experimental verification of the overall architecture is performed in relevant unknown Global Positioning System (GPS)-denied environments.

This article presents a dataset collected from the subterranean (SubT) environment with a current state-of-the-art sensors required for autonomous navigation. The dataset includes sensor measurements collected with RGB, RGB-D, event-based and thermal cameras, 2D and 3D lidars, inertial measurement unit (IMU), and ultra wideband (UWB) positioning systems which are mounted on the mobile robot. The overall sensor setup will be referred further in the article as a data collection platform. The dataset contains synchronized raw data measurements from all the sensors in the robot operating system (ROS) message format and video feeds collected with action and 360 cameras. A detailed description of the sensors embedded into the data collection platform and a data collection process are introduced. The collected dataset is aimed for evaluating navigation, localization and mapping algorithms in SubT environments. This article is accompanied with the public release of all collected datasets from the SubT environment. Link: Dataset (C) 2022 The Author(s). Published by Elsevier B.V.

In this article, we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an unmanned aerial vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on a nonlinear model predictive controller (NMPC) and utilizes an onboard 2-D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position space for the UAV. This article focuses also on the real-world implementation and experimental validation of the proposed reactive navigation scheme, and it is applied in multiple challenging laboratory experiments, where we also conduct comparisons with relevant methods of reactive obstacle avoidance. The solver utilized in the proposed approach is the optimization engine (OpEn) and the proximal averaged Newton for optimal control (PANOC) algorithm, where a penalty method is applied to properly consider obstacles and input constraints during the navigation task. The proposed novel scheme allows for fast solutions while using limited onboard computational power, which is a required feature for the overall closed-loop performance of a UAV and is applied in multiple real-time scenarios. The combination of built-in obstacle avoidance and real-time applicability makes the proposed reactive constrained navigation scheme an elegant framework for UAVs that is able to perform fast nonlinear control, local path planning, and obstacle avoidance, all embedded in the control layer.

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

This article proposes a novel strategy for detecting humans in harsh Sub-terranean (SubT) environments, with a thermal camera mounted on an aerial platform, based on the AlexNet Convolutional Neural Network (CNN). A transfer learning framework will be utilized for detecting the humans, where the aerial thermal images are fed to the trained network, which binary classifies them image content into two categories: a) human, and b) no human. Moreover, the AlexNet based framework is compared with two related popular CNN approaches as the GoogleNet and the Inception3Net. The efficacy of the proposed scheme has been experimentally evaluated through multiple data-sets, collected from a FLIR thermal camera during flights on an underground mining environment, fully demonstrating the performance and merits of the proposed module.

Geometry-morphing Micro Aerial Vehicles (MAVs) are gaining more attention lately, since, their ability to modify their geometric morphology in-flight increases their versatility while expanding their application range. In this novel research field, most of the works focus on the platform design and on the low-level control for maintaining stability after changing its shape. Nevertheless, another aspect of geometry morphing MAVs is the association of its morphology with respect to the shape and structure of the environment. In this article, we propose a novel Nonlinear Model Predictive Control (NMPC) structure that modifies the morphology of a quadrotor based on the environment entrances' geometrical shape. The proposed method considers restricted entrances as a constraint in the NMPC and modifies the arm configuration of the MAV to provide a collision-free path from the initial position to the desired goal while passing through the entrance. Multiple simulation results present the performance and efficiency of the proposed scheme in scenarios where the quadrotor is commanded to pass through narrow entrances.