This work deals with the problem of unlocking perpetual deployment capabilities for small-UAS robotics across the diverse settings of the real world and their challenges, encompassing considerations for marine environments alongside the more common terrestrial ones. Via the progress made within this scope, a step towards truly ubiquitous and selfsustainable aerial robotics is accomplished. The work consists of the development of the Gannet Solar-VTOL, a waterproof small-UAS that is capable of resting on the surface of water for prolonged periods of time and over varying temperature ranges, while harvesting solar power to recharge itself. Equally importantly, it integrates a field-proven Self-Sustainable Autonomous System architecture that allows it to hibernate and sustain its battery charge overnight or during periods of solar illumination scarcity, as well as to assess mission-critical parameters (e.g., water surface turbulence, ambient temperature of battery compartment) on the low-power side of the Power Management Stack, and react appropriately. Finally, the robot is equipped with an onboard camera and a Neural Processing Unit that allows it to perform in-field environmental monitoring operations (e.g., wildfire detection). This paper experimentally demonstrates the aforementioned capabilities, and concludes with a presentation of the amphibious small-UAS' long-term deployment within a marine environment in the N. Nevada region, spanning over 3 consecutive days.

This works deals with the problem of long-term autonomy in the context of multi-day field deployments of Micro Aerial Vehicle (MAV) systems. To truly depart from the necessity for human intervention for the crucial task of providing battery recharging, and to liberate from the need to operate in a confined range around specially installed infrastructure such as recharging pods, the MAV robot is required to harvest power on its own, but equally importantly also sustain prolonged periods of ambient power scarcity. This implies being able to sustain the battery charge overnight when using solar recharging, or even during multiple days of illumination inadequacy (e.g., due to degraded atmospheric lucidity and heavy overcast). We address this by presenting a Self-Sustainable Autonomous System architecture for MAVs centered around a specially tailored Power Management Stack, which is capable of achieving deep system hibernation, a feature that facilitates the aforementioned functionalities. We present a) continuous, b) multi-day successive, and c) externally-powered recharging that uses a legged robot-mounted Mobile Recharging Station. We conclude by demonstrating a challenging zero-intervention multi-day field deployment mission in the N.Nevada region.

This work presents a novel Aspect Ratio-Modular Vertical Take-Off and Landing (ARM-VTOL) aerial robot, which is a meta-aircraft composed of two or more TiltRotor hybrid aircraft systems capable of magnetically being coupled during hovering flight, and of executing VTOL / Fixed-Wing hybrid missions once combined. The proposed meta-aircraft system carries the advantage of improved aerodynamic efficiency due its increased cumulative planform aspect ratio, which can be leveraged to achieve prolonged flight times in collaborative multi-vehicle flight. We propose an extendable methodology for its control which relies on the multi-body equivalent dynamics, and we present the coupling mechanism design that facilitates its experimental demonstration. We accompany these contributions with a field test-driven evaluation study conducted with a bi-vehicle ARM-VTOL prototype. The presented sequence includes vehicle-to-vehicle magnetic coupling during hovering flight, and is followed by a combined-vehicle mission comprising vertical climb, VTOL-forward transition, fixed-wing flight and maneuvering, and reverse-transition to VTOL and landing.

This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems.

This paper presents a novel strategy for intelligent robotic environment reconfiguration applied to overcome mobility constraints with an autonomously exploring mobile manipulation system. A realistic problem arising during exploration of unknown challenging environments is the encountering of untraversable areas –given the robot’s mobility constraints– resulting in the robot getting stuck. We propose that given manipulation capabilities of an autonomous system, it should be possible to leverage loose entities in its surrounding to reconfigure its environment, and therefore potentially restore traversability to an unreachable region. This work’s contribution is two-fold: first, it proposes a mid-range traversability estimation graph-based backend which also allows early detection of terrain gaps, and secondly, it provides an algorithm for focused environment alteration that ensures stable and valid configurations. The plans of this generic policy are evaluated to decide if they resolve the robot’s problem, and are subsequently applied. The effectiveness of the proposed approach is demonstrated via experimental studies using a relevant autonomous system within a mobility-constrained mock-up environment.

This paper presents a novel strategy for the autonomous deployment of Micro Aerial Vehicle scouts through constricted aperture-like ingress points, by narrowly fitting and launching them with a high-precision Mobile Manipulation robot. A significant problem during exploration and reconnaissance into highly unstructured environments, such as indoor collapsed ones, is the encountering of impassable areas due to their constricted and rigid nature. We propose that a heterogeneous robotic system-of-systems armed with manipulation capabilities while also ferrying a fleet of microsized aerial agents, can deploy the latter through constricted apertures that marginally fit them in size, thus allowing them to act as scouts and resume the reconnaissance mission. This work’s contribution is twofold: first, it proposes active-vision based aperture detection to locate candidate ingress points and a hierarchical search-based aperture profile analysis to position a MAV’s body through them, and secondly it presents and experimentally demonstrates the novelty of a system-of-systems approach which leverages mobile manipulation to deploy other robots which are otherwise incapable of entering through extremely narrow openings.

In this work we present a new methodology on learning-based path planning for autonomous exploration of subterranean environments using aerial robots. Utilizing a recently proposed graph-based path planner as a "training expert" and following an approach relying on the concepts of imitation learning, we derive a trained policy capable of guiding the robot to autonomously explore underground mine drifts and tunnels. The algorithm utilizes only a short window of range data sampled from the onboard LiDAR and achieves an exploratory behavior similar to that of the training expert with a more than an order of magnitude reduction in computational cost, while simultaneously relaxing the need to maintain a consistent and online reconstructed map of the environment. The trained path planning policy is extensively evaluated both in simulation and experimentally within field tests relating to the autonomous exploration of underground mines.

This paper proposes an approach for fusing direct radiometric data from a thermal camera with inertial measurements to extend the robotic capabilities of aerial robots for navigation in GPS-denied and visually degraded environments in the conditions of darkness and in the presence of airborne obscurants such as dust, fog and smoke. An optimization based approach is developed that jointly minimizes the re-projection error of 3D landmarks and inertial measurement errors. The developed solution is extensively verified against both ground-truth in an indoor laboratory setting, as well as inside an underground mine under severely visually degraded conditions.

In this paper the problem of contact-based navigation path planning for aerial robots is considered with the goal of enabling the autonomous in-contact operation on surfaces that can be highly anomalous. Such a capacity can prove critical in inspection through contact missions, as well as when a flying robot is tasked to operate in very narrow environments rendering safe free-flight impossible. To achieve this objective, beyond sliding in contact, a new locomotion primitive is introduced, namely that of azimuth rotations perpendicular to the surface under consideration. This new navigation mode, called flying cartwheel mode, offers navigation resourcefulness and resilience when the system is tasked to move in contact with surfaces that are otherwise non-traversable. The designed path planning method exploits both navigation modalities and a traversability metric to decide when to switch from sliding to flying cartwheel mode, and overall provides cost-optimal trajectories for in-contact navigation. The proposed approach is verified both in simulation, as well as experimentally using a surface presenting complex anomalies. It is highlighted that the proposed method does not assume any specialized contact mechanism or a control law tailored to physical interaction tasks, and hence is applicable to almost any micro aerial vehicle integrating protective shrouds around its propellers.

This paper details the system and methods designed to enable the autonomous estimation of distributed nuclear radiation fields within complex and possibly GPS-denied environments. A sensing apparatus consisting of three radially placed Thallium-doped Cesium Iodide (CsI(Tl)) scintillators and Silicon Photomultipliers (SiPm) combined with custom- built pulse counting circuitry is designed and the provided readings are pose-annotated using LiDAR-based localization. Given this capacity, a method that utilizes the radiation intensity readings to first calculate the immediate field gradient and then combine this information to update and co-estimate the believed field intensity and gradient across the whole environment is developed. The strategy propagates the effect of each local measurement through field gradient co-estimation and simultaneously derives a model of the underlying uncertainty. To further support the need for informative data gathering, especially in the framework of emergency and rapid reconnaissance missions, a path planning strategy is also developed that first utilizes the field intensity and uncertainty estimates to select its new waypoint and then performs terrain traversability analysis to derive admissible paths. The complete system is evaluated both in simulation and experimentally. The experimental results refer to the autonomous exploration and field estimation inside an indoor facility within which actual radioactive uranium and thorium ore sources have been distributed.

This paper presents a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner is structured around a bifurcated local-and global-planner architecture. The local planner employs a rapidly-exploring random graph to reliably and efficiently identify collision-free paths that optimize an exploration gain within a local subspace. Accounting for the robot endurance limitations and the possibility that the local planner reaches a dead-end (e.g. a mine heading), the global planner is engaged when a return-to-home path must be derived or when the robot should be re-positioned towards an edge of the exploration space. The proposed planner is field evaluated in a collection of deployments inside both active and abandoned underground mines in the U.S. and in Switzerland.

This paper presents a novel strategy for autonomous visual saliency-aware receding horizon exploration of unknown environments using aerial robots. Through a model of visual attention, incrementally built maps are annotated regarding the visual importance and saliency of different objects and entities in the environment. Provided this information, a path planner that simultaneously optimizes for exploration of unknown space, and also directs the robot's attention to focus on the most salient objects, is developed. Following a two-step optimization paradigm, the algorithm first samples a random tree and identifies the branch maximizing for new volume to be explored. The first viewpoint of this path is then provided as a reference to the second planning step. Within that, a new tree is spanned, admissible branches arriving at the reference viewpoint while respecting a time budget dependent on the robot endurance and its environment exploration rate are found and evaluated in terms of reobserving salient regions at sufficient resolution. The best branch is then selected and executed by the robot, and the whole process is iteratively repeated. The proposed method is evaluated regarding its ability to provide increased attention toward salient objects, is verified to run onboard a small aerial robot, and is demonstrated in a set of challenging experimental studies.

This paper details the system and methods developed to enable autonomous nuclear radiation source localization and mapping using aerial robots in GPS-denied environments. A Thallium-doped Cesium Iodide (CsI(Tl)) scintillator and a Silicon Photomultiplier are combined with custom-built electronics for counting and spectroscopy, and the provided radiation measurements are pose-annotated using visual-inertial localization enabling autonomous operation in GPS-denied environments. Provided this capability, a strategy for radioactive source localization, as well as active source search path planning was developed. The proposed method is motivated and accounts for the limited endurance of the vehicle, which entails a very small amount of dwell points, and the fact that GPS-denied localization implies varying uncertainty of the robot's position estimate. The complete system is evaluated in multiple experimental studies using a small aerial robot and a Cesium-137 radiation source. As shown, accurate radioactive source localization is achieved, enabling efficient radiation mapping of indoor GPS-denied environments.

This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds the branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization and mapping uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated. The proposed planner is capable of running online onboard a small aerial robot and its performance is evaluated using experimental studies in a challenging environment.

This paper proposes a methodology for visual tracking of a dynamic generalized subject within an unknown map, by relying on its perception as a separate entity which can be distinguished spatially and visually from its environment. To this purpose, a 3D-representation of the visible scenery is examined, and the subject is spatially identified by its externally viewed hull via a mesh-connection algorithm aided by visual cues, and visually identified by distinct feature tracking based on an incrementally built list of key-aspects. These two processes operate in closed-loop, and employing a set of assumptions regarding the subject's structural/temporal invariance the tracking health state can be determined. This work additionally presents the framework for the deployment of this scheme for autonomous aerial robotic subject tracking, employing the dynamic subject/environment distinction to obtain knowledge of the environment structure, and collision-free trajectory generation algorithms to achieve mobile tracking.

The issue of efficient large force and moment exertion with Unmanned Aerial Vehicles (UAVs) is the subject of this paper. Inspiration is drawn from the vision of UAVs that are capable of autonomously executing industrial activities, or effectively reconfiguring their environment via forceful interaction. Therein, the technical shortcomings of the potential utilization of conventional underactuated UAV platform designs are examined, in terms of operational effectiveness-versus-safety. The innovative implementation of the direct thrust-vectoring authority of tiltrotor UAV types for forceful interaction is proposed, and its associated technical contributions are analyzed. A methodology is developed for controlled forward thrust force and rotating moment exertion, while ensuring safe operation near the hovering attitude pose. A large force-requiring scenario is assembled, consisting of a realistically-sized object laid on solid ground, regarded as a path-hindering obstacle to be forcefully removed by the UAV via pushing manipulation. To this purpose, a high-end autonomous tiltrotor UAV is employed in order to achieve this environment modification task, relying on a properly synthesized control structure.

The experimental translational hovering control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This novel UAV is developed to possess the capability to perform autonomous conversion between the Vertical Take-Off and Landing, and the Fixed-Wing flight modes. Via this design's implemented features however, the capability for additional control authority on the UAV's longitudinal translational motion arises: The rotor-tilting servos are utilized in performing thrust vectoring of the main rotors, thus exploiting their fast response characteristics in directly providing translation-controlling forces. The system's hovering translation is handled by a Model Predictive Control scheme, following the aforementioned actuation principles. While performing experimental studies of the overall controlled system's efficiency, the advantageous effects of this novel control authority are clearly noted. Additionally, in this article the considerations and requirements for operational autonomy-related on-board-only state estimation are addressed.

The optimal trajectory-tracking control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This specific UAV design possesses the capability to control the orientation of its main rotors, thus enabling operation in both the Vertical Take-Off and Landing as well as the Fixed-Wing flight mode configuration. The translational controller developed is based on a Linear-Quadratic tracking scheme. Additionally to the proposed controller, the newly introduced capability for rotor-tilting, and thus thrust vectoring, as provided by this design is proposed for its utilization in the control of the longitudinal degree-of-freedom of the UAV. Simulation and experimental results are presented, demonstrating both the overall proposed controller's efficiency, as well as the clear advantage gained by the aforementioned proposed strategy, with regard to the controlled system's longitudinal control performance.