This paper introduces a safety filter to ensure collision avoidance for multirotor aerial robots. The proposed formalism leverages a single Composite Control Barrier Function from all position constraints acting on a third-order nonlinear representation of the robot's dynamics. We analyze the recursive feasibility of the safety filter under the composite constraint and demonstrate that the infeasible set is negligible. The proposed method allows computational scalability against thousands of constraints and, thus, complex scenes with numerous obstacles. We experimentally demonstrate its ability to guarantee the safety of a quadrotor with an onboard LiDAR, operating in both indoor and outdoor cluttered environments against both naive and adversarial nominal policies.

Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based “in-flight” attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.

This paper presents a learned model to predict the robot-centric velocity of an underwater robot through dynamics-aware proprioception. The method exploits a recurrent neural network using as inputs inertial cues, motor commands, and battery voltage readings alongside the hidden state of the previous time-step to output robust velocity estimates and their associated uncertainty. An ensemble of networks is utilized to enhance the velocity and uncertainty predictions. Fusing the network's outputs into an Extended Kalman Filter, alongside inertial predictions and barometer updates, the method enables long-term underwater odometry without further exteroception. Furthermore, when integrated into visual-inertial odometry, the method assists in enhanced estimation resilience when dealing with an order of magnitude fewer total features tracked (as few as 1) as compared to conventional visual-inertial systems. Tested onboard an underwater robot deployed both in a laboratory pool and the Trondheim Fjord, the method takes less than 5 ms for inference either on the CPU or the GPU of an NVIDIA Orin AGX and demonstrates less than 4% relative position error in novel trajectories during complete visual blackout, and approximately 2% relative error when a maximum of 2 visual features from a monocular camera are available.

This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance in trajectory-tracking tasks with UAVs. The network is trained on simulated depth images to output a collision score for queried 3D points within the sensor field of view. Then, this network is translated into an algebraic symbolic equation and included in the N-MPC, explicitly constraining predicted positions to be collision-free throughout the receding horizon. The N-MPC achieves real time control of a UAV with a control frequency of 100Hz. The proposed framework is validated through statistical analysis of the collision classifier network, as well as Gazebo simulations and real experiments to assess the resulting capabilities of the N-MPC to effectively avoid collisions in cluttered environments. The associated code is released open-source.

This work contributes a novel deep navigation policy that enables collision-free flight of aerial robots based on a modular approach exploiting deep collision encoding and reinforcement learning. The proposed solution builds upon a deep collision encoder that is trained on both simulated and real depth images using supervised learning such that it compresses the high-dimensional depth data to a low-dimensional latent space encoding collision information while accounting for the robot size. This compressed encoding is combined with an estimate of the robot’s odometry and the desired target location to train a deep reinforcement learning navigation policy that offers low-latency computation and robust sim2real performance. A set of simulation and experimental studies in diverse environments are conducted and demonstrate the efficiency of the emerged behavior and its resilience in real-life deployments.

Enabling autonomous robots to operate robustly in challenging environments is necessary in a future with increased autonomy. For many autonomous systems, estimation and odometry remains a single point of failure, from which it can often be difficult, if not impossible, to recover. As such robust odometry solutions are of key importance. In this work a method for tightly-coupled LiDAR-Radar-Inertial fusion for odometry is proposed, enabling the mitigation of the effects of LiDAR degeneracy by leveraging a complementary perception modality while preserving the accuracy of LiDAR in well-conditioned environments. The proposed approach combines modalities in a factor graph-based windowed smoother with sensor information-specific factor formulations which enable, in the case of degeneracy, partial information to be conveyed to the graph along the non-degenerate axes. The proposed method is evaluated in real-world tests on a flying robot experiencing degraded conditions including geometric self-similarity as well as obscurant occlusion. For the benefit of the community we release the datasets presented: https://github.com/ntnu-arl/lidar_degeneracy_datasets.

This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index n and radial pixel coordinate) and a virtual pinhole model (as a function of n). We derive the self-calibration formulation leveraging epipolar constraints to estimate the refractive index and subsequently correct for distortion. Through experimental studies using an underwater robot integrating cameras and inertial sensing, the model is validated regarding the accurate estimation of the refractive index and its benefits for robust odometry estimation in an extended envelope of conditions. Lastly, we show the transition between media and the estimation of the varying refractive index online, thus allowing computer vision tasks across refractive media.

Autonomous robot navigation can be particularly demanding, especially when the surrounding environment is not known and safety of the robot is crucial. This work relates to the synthesis of Control Barrier Functions (CBFs) through data for safe navigation in unknown environments. A novel methodology to jointly learn CBFs and corresponding safe controllers, in simulation, inspired by the State Dependent Riccati Equation (SDRE) is proposed. The CBF is used to obtain admissible commands from any nominal, possibly unsafe controller. An approach to apply the CBF inside a safety filter without the need for a consistent map or position estimate is developed. Subsequently, the resulting reactive safety filter is deployed on a multirotor platform integrating a LiDAR sensor both in simulation and real-world experiments.

This paper presents a general refractive camera model and online co-estimation of odometry and the refractive index of an unknown media. This enables operation in diverse and varying refractive fluids, given only the camera calibration in air. The refractive index is estimated online as a state variable of a monocular visual-inertial odometry framework in an iterative formulation using the proposed camera model. The method was verified on data collected using an underwater robot traversing inside a pool. The evaluations demonstrate convergence to the ideal refractive index for water despite significant perturbations in the initialization. Simultaneously, the approach enables on-par visual-inertial odometry performance in refractive media without prior knowledge of the refractive index or requirement of medium-specific camera calibration.

This article surveys recent progress and discusses future opportunities for simultaneous localization and mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary exploration, search and rescue, disaster response, and automated mining, among others. SLAM in underground environments has recently received substantial attention, thanks to theDARPA Subterranean (SubT) Challenge, a global robotics competition aimed at assessing and pushing the state of the art in autonomous robotic exploration and mapping in complex underground environments. This article 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 article has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on light detection and ranging (LIDAR)-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multirobot 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, which 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.

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that together ensure that a complete mesh model is reconstructed for each semantic, while its surfaces are observed at appropriate resolution and through suitable viewing angles. Evaluated in extensive simulation studies and experimental results using a flying robot, the planner delivers efficient combined exploration and high-fidelity inspection planning that is focused on the semantics of interest. Comparisons against relevant methods of the state-of-the-art are further presented.

Autonomous operation in underwater environ-ments is, arguably, one of the most complex domains. It requires safe operations under the presence of unpredictable surge, currents, uncertainty, and dynamic obstacles that challenges to the highest degree real-time motion planning; the primary focus of this paper. Although previous work addressed the problem of safe real-time 3D navigation in cluttered underwater environments, it did not account explicitly for disturbances, currents, dynamic obstacles, or uncertainty growth. This paper presents ResiPlan, a novel motion planning framework that utilizes past information of errors monitoring the path follower's performance, along with estimation of dynamic obstacles and uncertainty, to produce adaptive paths by adjusting the safety margins accordingly. Extensive numerical experiments and simulations validate the safety guarantees of the technique, in a variety of different environments with various types of disturbance, showcasing the strong potential to be utilized for operations in challenging underwater environments.

This paper contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both real-world and simulated depth images to compress the input data, while preserving semantically-labeled thin obstacles and handling invalid pixels in the depth sensor's output. This compressed representation, in addition to the robot's partial state involving its linear/angular velocities and its attitude are then utilized to train an uncertainty-aware 3D Collision Prediction Network in simulation to predict collision scores for candidate action sequences in a predefined motion primitives library. A set of simulation and experimental studies in cluttered environments with various sizes and types of obstacles, including multiple hard-to-perceive thin objects, were conducted to evaluate the performance of the proposed method and compare against an end-to-end trained baseline. The results demonstrate the benefits of the proposed semantically-enhanced deep collision prediction for learning-based autonomous navigation.

This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.

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 framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide loosely-coupled fusion of any number of sensing modalities. Tailored to the goal of enabling resilient robotic autonomy in GPS-denied and perceptually-degraded environments, MIMOSA currently contains modules for pointcloud registration, fusion of multiple odometry estimates relying on visible-light and thermal vision, as well as inertial measurement propagation. A flexible back-end utilizes the estimates from various modalities as relative transformation factors. The method is designed to be robust to degeneracy through the maintenance and tracking of modality-specific health metrics, while also being inherently tolerant to sensor failure. We detail this framework alongside our implementation for handling high-rate asynchronous sensor measurements and evaluate its performance on data from autonomous subterranean robotic exploration missions using legged and aerial robots.

This paper contributes a novel strategy towards risk-aware motion planning for collision-tolerant aerial robots subject to localization uncertainty. Attuned to the fact that micro aerial vehicles are often tasked to navigate within GPS-denied, possibly unknown, confined and obstacle-filled environments the proposed method exploits collision-tolerance at the robot design level to mitigate the risks of collisions especially as their likelihood increases with growing uncertainty. Accounting for the maximum kinetic energy with which an impact is considered safe, alongside the robot dynamics, the planner builds a set of admissible uncertainty-aware and collision-inclusive paths over a horizon involving multiple motion steps. The first step of the best path is executed by the robot, while the procedure is then repeated in a receding horizon manner. Evaluated in extensive simulation studies and experimental results with a collision-tolerant flying robot, the planner successfully considers the interplay between uncertainty and the likelihood of a collision, balances the risks of possible impacts and enables to navigate safely within highly cluttered environments.

In this work, we present an adaptive behavior path planning method for autonomous exploration and visual search of unknown environments. As volumetric exploration and visual coverage of unknown environments, with possibly different sensors, are non-identical objectives, a principled combination of the two is proposed. In particular, the method involves three distinct planning policies, namely exploration, and sparse or dense visual coverage. A hypergame formulation is proposed which allows the robot to select for the next-best planning behavior in response to the currently encountered environment challenges in terms of geometry and visual conditions, alongside a self-assessment of its performance. The proposed planner is evaluated in a collection of experimental and simulation studies in diverse environments, while comparative results against a state-of-the-art exploration method are also presented.

In this paper, we develop an online learning-based visual tracking framework that can optimize the target model and estimate the scale variation for object tracking. We propose a recommender-based tracker, which is capable of selecting the representative convolutional neural network (CNN) layers and feature maps autonomously. In addition, the proposed recommender computes the weights of these layers and feature maps. A discriminative target percept of each recommended layer is reconstructed by the weighted sum of the recommended feature maps. Then the target model of the correlation filter is updated by the weighted sum of the target percepts. Thus, a sub-network is extracted from the pre-trained CNN backbone for the tracking process of a specific target. To deal with scale changes, we propose a spatiotemporal-based min-channel method to estimate the target size variation directly from CNN features. Experimental results on 50 benchmark datasets and video data from a rescue drone demonstrate that the proposed tracker is quite competitive with the state-of-the-art CNN-based trackers in terms of accuracy, scale adaptation, and robustness for UAV-related applications.

This paper contributes a method designed to enable autonomous distributed 3D nuclear radiation field mapping. The algorithm uses a single radiation sensor and a sequence of spatially distributed and robotically acquired radiation measurements across a discretized 3D grid to derive a radiation gradient. The derived gradient is probabilistically propagated to unknown components of the map to further guide a curiosity-driven path planner by identifying the next most radiologically informative point given available information. To demonstrate the method, we develop a resilient micro flying robot capable of autonomous GPS-denied navigation that integrates a Thallium–doped Cesium Iodide (CsI(Tl)) scintillator and Silicon Photomultiplier (SiPm) combined with custom–built pulse counting circuitry. A set of experimental studies is presented inside an indoor facility within which actual radioactive uranium ore sources have been distributed.

This work presents the design and autonomous navigation policy of the Resilient Micro Flyer, a new type of collision-tolerant robot tailored to fly through extremely confined environments and manhole-sized tubes. The robot maintains a low weight (<500g) and implements a combined rigid-compliant design through the integration of elastic flaps around its stiff collision-tolerant frame. These passive flaps ensure compliant collisions, contact sensing and smooth navigation in contact with the environment. Focusing on resilient autonomy, capable of running on resource-constrained hardware, we demonstrate the beneficial role of compliant collisions for the reliability of the onboard visual-inertial odometry and propose a safe navigation policy that exploits both collision-avoidance using lightweight time-of-flight sensing and adaptive control in response to collisions. The robot further realizes an explicit manhole navigation mode that exploits the direct mechanical feedback provided by the flaps and a special navigation strategy to self-align inside manholes with non-straight geometry. Comprehensive experimental studies are presented to evaluate, both individually and as a whole, how resilience is achieved based on the robot design and its navigation scheme.

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset the Highway Traffic Anomaly (HTA) dataset- for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.

This paper overviews the system design, modeling and control of the Aerial Robotic Chain. This new design corresponds to a reconfigurable robotic system of systems consisting of multilinked micro aerial vehicles that presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potential of distributed sensing and processing, and enable system extendability. We present the system dynamics for any number of connected aerial vehicles, followed by the controller design involving a model predictive position control loop combined with multiple parallel angular controllers on SO(3). Evaluation studies both in simulation and through experiments based on our ARC-Alpha prototype are depicted and involve coordinated maneuvering and shape configuration to cross narrow windows.

This paper presents a novel path planning strategy for fast and agile exploration using aerial robots. Tailored to the combined need for large-scale exploration of challenging and confined environments, despite the limited endurance of micro aerial vehicles, the proposed planner employs motion primitives to identify admissible paths that search the configuration space, while exploiting the dynamic flight properties of small aerial robots. Utilizing a computationally efficient volumetric representation of the environment, the planner provides fast collision-free and future-safe paths that maximize the expected exploration gain and ensure continuous fast navigation through the unknown environment. The new method is field-verified in a set of deployments relating to subterranean exploration and specifically, in both modern and abandoned underground mines in Northern Nevada utilizing a 0.55m-wide collision-tolerant flying robot exploring with a speed of up to 2m/s and navigating sections with width as small as 0.8m.

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 addresses the problem of trajectory optimization for the transition of a Vertical Take-off and Landing (VTOL) tailsitter Unmanned Aerial Vehicle (UAV). The proposed strategy performs a model based optimization, where the model represents the closed-loop dynamics of the UAV with low-level control, ensuring attitude stabilization over the whole trajectory. We discuss the design of an optimization framework, vehicle modeling, and elaborate on the cost function construction. An additional feedback gain is implemented on the throttle channel with altitude discrepancies as its input to provide some level of robustness to wind disturbances. The overall approach is verified in simulation and experimental results with a focus on optimization of the back-transition (cruise-to-hover) of the Wingtra S100 VTOL tailsitter.

This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.

This paper addresses the challenges of the design, development and control of a new convertible VTOL tailsitter unmanned aerial vehicle that combines the advantages of both fixed wing and rotary wing systems. Wind tunnel measurements are used to get an understanding of the control allocation and to model the static forces and moments acting on the system. Based on the derived model, a novel controller that operates in SO(3) and handles the dynamics of the vehicle at any attitude configuration, including the rotorcraft and fixed-wing regimes as well as their transitions, is presented. This unified controller allows the autonomous transition of the system without discontinuities of switching, as well as its overall high performance flight control. The capabilities and flying qualities of the platform and the controller are demonstrated and evaluated by means of extensive experimental studies.

In this paper, the design, modeling and experimental verification of a large workspace, parallel aerial manipulator is presented. The proposed manipulator has 3 Degrees of Freedom (DoFs) and enables physical interaction on the sides, as well as below the aerial robot. The design parameters of the manipulator are chosen such that it achieves large and singularity-free workspace in combination with high dexterity. A Global Conditioning Index (GCI) is defined and used as a performance index for the manipulator over its complete workspace. Given the manipulator design parameters, a holistic model of the redundant aerial manipulation system is derived capturing the coupled dynamics of the aerial vehicle and the manipulator. Free flight experimental studies are utilized to validate the system, demonstrate its redundant DoFs, and evaluate its performance.

Within this paper, a new fast algorithm that provides efficient solutions to the problem of inspection path planning for complex 3D structures is presented. The algorithm assumes a triangular mesh representation of the structure and employs an alternating two-step optimization paradigm to find good viewpoints that together provide full coverage and a connecting path that has low cost. In every iteration, the viewpoints are chosen such that the connection cost is reduced and, subsequently, the tour is optimized. Vehicle and sensor limitations are respected within both steps. Sample implementations are provided for rotorcraft and fixed-wing unmanned aerial systems. The resulting algorithm characteristics are evaluated using simulation studies as well as multiple real-world experimental test-cases with both vehicle types.

This paper presents the conceptual design, detailed development and flight testing of AtlantikSolar, a 5.6m-wingspan solar-powered Low-Altitude Long-Endurance (LALE) Unmanned Aerial Vehicle (UAV) designed and built at ETH Zurich. The UAV is required to provide perpetual endurance at a geographic latitude of 45°N in a 4-month window centered around June 21st. An improved conceptual design method is presented and applied to maximize the perpetual flight robustness with respect to local meteorological disturbances such as clouds or winds. Airframe, avionics hardware, state estimation and control method development for autonomous flight operations are described. Flight test results include a 12-hour flight relying solely on batteries to replicate night-flight conditions. In addition, we present flight results from Search-And-Rescue field trials where a camera and processing pod were mounted on the aircraft to create high-fidelity 3D-maps of a simulated disaster area.

The challenge of aerial robotic physical interaction towards inspection of infrastructure facilities through contact is the main motivation of this paper. A hybrid model predictive control framework is proposed, based on which a typical quadrotor vehicle becomes capable of stable physical interaction, accurate trajectory tracking on environmental surfaces as well as force control with only minor structural adaptations. Convex optimization techniques enabled the explicit computation of such a controller which accounts for the dynamics in free-flight and during physical interaction, ensures the stability of the hybrid system as well as response optimality, while respecting system constraints and imposed logical rules. This control framework is further extended to include obstacle avoidance capabilities. Extensive experimental studies that included complex “aerial-writing” tasks, interaction with non-planar and textured surfaces and obstacle avoidance maneuvers, indicate the efficiency of the approach and the potential capabilities of such aerial robotic physically interacting operations.

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.

A new type of coaxial-rotor unmanned helicopter capable of physically interacting with its environment is the subject of this paper. Its design is optimized in order to provide the means of robust environmental interaction through contact (e.g. docking and sliding on walls). Due to the rapid change of the dynamics from the free-flying helicopter to the helicopter subject to the forces and moments during contact a hybrid systems modeling approach is followed. This global model of the system's dynamics is the basis for the design of a hybrid model predictive controller that guarantees the stability of the hybrid system and provides the capability of controlled docking on walls as well as sliding on them. The capabilities of the platform and the efficiency of the control law are illustrated through experimental studies.

This paper describes an accurate and extendable rotorcraft dynamics simulator developed to support the design and control of autonomous coaxial rotor vehicles. This simulator is capable of accurately predicting the dynamic flight response of coaxial rotor vehicles purely based on geometric, inertial and aerodynamic specifications. The simulator is fully configurable and implements the typical mechanical layouts found in model-size coaxial helicopters. The corresponding software framework as well as the underlying theory is presented in detail. System parameters for a coaxial rotor prototype have been estimated and the resulting simulation results compared with real flight-data demonstrating the capabilities of the presented simulation software.

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.

This video presents experiments conducted within the final review meeting demonstration session of the AIRobots project. AIRobots started at 2010 and the final review meeting took place on 22 of March, 2013. The presented experiments cover a wide area of the challenges related with aerial industrial inspection. In particular, multiple test-cases related with both vision-based and contact-based inspection and in general physical interaction are shown. It is highlighted that these experiments were recorded live during the project demonstration and evaluation process.

In this article, the design, system modeling and control of a new hybrid type of Unmanned Aerial Vehicle (UAV) is presented. Based on the flight principles of the Dos Samara UAV, a new vehicle that combines the capability of hovering, like a helicopter, and high speed-increased endurance flying, like a fixed-wing aircraft, is designed. The nonlinear dynamics model of the aircraft operating in helicopter mode is derived and linearized around hovering operation. Based on this model an LQ-controller is designed. The performance of the overall system is examined in simulation studies.

In this paper the design and the experimental verification of a Constrained Finite Time Optimal (CFTO) control scheme for the attitude control of an Unmanned Quadrotor Helicopter (UqH) subject to wind gusts is being presented. In the proposed design the UqH has been modeled by a set of Piecewise Affine (PWA) linear equations while the wind gusts effects are embedded in the system model description as the affine terms. In this approach the switching among the PWA model descriptions are ruled by the rate of the rotation angles. In the design of the stabilizing CFTO-controller both the magnitude of external disturbances (worst case applied wind gust), and the mechanical constraints of the UqH such as maximum thrust in the rotors and UqH's angles rate are taken under consideration in order to design an off-line controller that could rapidly be applied to a UqH in a form of a look-up table. The proposed control scheme is applied in experimental studies and multiple test-cases are presented that prove the efficiency of the proposed scheme.