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Some human-machine systems are designed so that machines (robots) gather and deliver data to remotely located operators (humans) through an interface to aid them in classification. The performance of a human as a (binary) classifier-in-the-loop is characterized by probabilities of correctly classifying objects (or points of interest) as a true target or a false target. These two probabilities depend on the time spent collecting information at a point of interest (POI), known as dwell time. The information gain associated with collecting information at a POI is then a function of dwell time and discounted by the revisit time, i.e., the duration between consecutive revisits to the same POI, to ensure that the vehicle covers all POIs in a timely manner. The objective of the routing problem for classification is to route the vehicles optimally, which is a discrete problem, and determine the optimal dwell time at each POI, which is a continuous optimization problem, to maximize the total discounted information gain while visiting every POI at least once. Due to the coupled discrete and continuous problem, which makes the problem hard to solve, we make a simplifying assumption that the information gain is discounted exponentially by the revisit time; this assumption enables one to decouple the problem of routing with the problem of determining optimal dwell time at each POI for a single vehicle problem. For the multi-vehicle problem, since the problem involves task partitioning between vehicles in addition to routing and dwell time computation, we provide a fast heuristic to obtain high-quality feasible solutions. [ABSTRACT FROM AUTHOR]

In terms of digital transformation, organizations today are aware of the critical role that data and information play in their expansion and development in light of the Internet of Things. To increase network performance and stability, many applications are moving from cloud computing to edge computing (EC). However, in order to satisfy customers, applications like intelligent transportation systems, smart grids, smart cities, and healthcare call for even more effective services. This survey addresses extensive research on two aspects: firstly, we present the advancements of two application domains namely maritime areas and aerial systems in terms of integration with EC architecture. Secondly, we cover the most recent technologies, artificial intelligence (AI) and blockchain, combined into the EC paradigm by discussing several experiments conducted in various fields to demonstrate the value of utilizing them in the edge computing architecture. We analyze the results of eleven experiments in each technology from 2015 to 2023.

Path planning for quadcopters has been proven to be one kind of NP-hard problem with huge search space and tiny feasible solution range. Metaheuristic algorithms are widely used in such types of problems for their flexibility and effectiveness. Nevertheless, most of them cannot meet the needs in terms of efficiency and suffer from the limitations of premature convergence and local minima. This paper proposes a novel algorithm named Heuristic Hybrid Particle Swarm Optimization (HHPSO) to address the path planning problem. On the heuristic side, we use the control points of cubic b-splines as variables instead of waypoints and establish some heuristic rules during algorithm initialization to generate higher-quality particles. On the hybrid side, we introduce an iteration-varying penalty term to shrink the search range gradually, a Cauchy mutation operator to improve the exploration ability, and an injection operator to prevent population homogenization. Numerical simulations, physical model-based simulations, and a real-world experiment demonstrate the proposed algorithm’s superiority, effectiveness and robustness.

Perching in unmanned aerial vehicles (UAVs) offers the possibility of extending the range of aerial robots beyond the limits of their batteries. It has been a topic of intense study for multirotor UAVs. Perching in winged UAVs is harder because a kinetic energy balance has to be struck. Reducing too much energy results in the vehicle stalling and falling. Too much kinetic energy at touchdown could damage the vehicle. Most studies used dangerous pitch‐up maneuvers to manage this balance. This work presents a system that eliminates the pitch‐up maneuver by mechanically capturing and storing kinetic energy at impact. It is validated using a passive mechanical system consisting of a storage mechanism for energy recuperation and a claw for perching on a horizontal rod. The energy stored in the mechanism is then used to unperch. The mathematical model for the recuperation strategy is presented and perching success at various approach attitudes are characterized. The proof‐of‐concept claw recaptures 5% of the kinetic energy during perching. Experiments indicate that the device can successfully perch at a wide range of yaw angles, but requires more precision in roll. We show that our perching mechanism enables the fastest UAV perching to date (7.4 m s−1). [ABSTRACT FROM AUTHOR]

In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

Perching in unmanned aerial vehicles (UAVs) offers the possibility of extending the range of aerial robots beyond the limits of their batteries. It has been a topic of intense study for multirotor UAVs. Perching in winged UAVs is harder because a kinetic energy balance has to be struck. Reducing too much energy results in the vehicle stalling and falling. Too much kinetic energy at touchdown could damage the vehicle. Most studies used dangerous pitch‐up maneuvers to manage this balance. This work presents a system that eliminates the pitch‐up maneuver by mechanically capturing and storing kinetic energy at impact. It is validated using a passive mechanical system consisting of a storage mechanism for energy recuperation and a claw for perching on a horizontal rod. The energy stored in the mechanism is then used to unperch. The mathematical model for the recuperation strategy is presented and perching success at various approach attitudes are characterized. The proof‐of‐concept claw recaptures 5% of the kinetic energy during perching. Experiments indicate that the device can successfully perch at a wide range of yaw angles, but requires more precision in roll. We show that our perching mechanism enables the fastest UAV perching to date (7.4 m s−1).

In this article, we present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial–temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization and the backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude. [ABSTRACT FROM AUTHOR]

This work focuses on catching safely an aerial micro-robot in mid-air using another aerial robot that is equipped with a universal soft gripper. To avoid aerodynamic disturbances such as downwash, that would push the target robot away, we follow a horizontal grasping approach. To this end, the article introduces a gripper design based on soft actuators that can stay horizontally straight with a single fixture and maintain sufficiently compliance in order to bend when air pressure is applied. Further, we develop the Soft Aerial Gripper (SoAG), an open-source aerial robot equipped with the developed soft end-effector and that features an onboard pneumatic regulation system. Experimental results show that the developed low-cost soft gripper has fast opening and closing responses despite being powered by lightweight air pumps, responses that are comparable to those of a commercially available end-effector tested we test against. Static grasping tests study the soft gripper’s robustness in capturing aerial micro-robots under aerodynamic disturbances. We experimentally demonstrated the feasibility of using the SoAG robot to catch a hovering micro-robot with or without propeller guards. The feasibility of dynamic catching is also shown by capturing a moving aerial micro-robot with a velocity of 0.2 m/s. The free flight performance of the SoAG robot is studied against a conventional quadrotor and in different gripper and payload status.

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and, hence, increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adaptable to emerging LiDARs of different scanning patterns; the second main novelty is maintaining a map by an incremental k-dimensional (k-d) tree data structure, incremental k-d tree (ikd-Tree), that enables incremental updates (i.e., point insertion and delete) and dynamic rebalancing. Compared with existing dynamic data structures (octree, R $^\ast$ -tree, and nanoflann k-d tree), ikd-Tree achieves superior overall performance while naturally supports downsampling on the tree. We conduct an exhaustive benchmark comparison in 19 sequences from a variety of open LiDAR datasets. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems. Various real-world experiments on solid-state LiDARs with small field of view are also conducted. Overall, FAST-LIO2 is computationally efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multiline spinning and solid-state LiDARs, unmanned aerial vehicle (UAV) and handheld platforms, and Intel- and ARM-based processors), while still achieving a higher accuracy than existing methods. Our implementation of the system FAST-LIO2 and the data structure ikd-Tree are both open-sourced on Github. [ABSTRACT FROM AUTHOR]

We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned deep neural networks with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: 1) Spectral normalization for stability and generalization guarantees of unseen data and 2) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multirobot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors. [ABSTRACT FROM AUTHOR]

Large scale aerial deployment of miniature sensors in tough environmental conditions requires a deployment device that is lightweight, robust, and steerable. We present a novel samara-inspired autorotating craft that is capable of two flight modes (autorotating mode and diving mode) with an average glide angle of 28.9 $^{\circ }$ (1.81 m lateral distance per 1 m loss of altitude) in the former mode. The bidirectional transition between the two modes and directional control is achieved by using only a single actuator. Also, in order to minimize its glide angle, a design optimization methodology is presented for our prototype, diving samara autorotating wing, along with a new cyclic control strategy for directional control of autorotating descent. The dynamic model, simulated in a six degrees-of-freedom environment using the blade element theory, is integrated with genetic algorithm to derive parameters for the wing geometry, flap angle for autorotation, and the proposed cyclic control. The physical prototype autorotates at a descent velocity of 1.43 m/s and rotation speed 4.17 Hz, and is able to transit to diving mode in an average duration of 272 ms to increase its descent velocity by at least 17.6 times. At any point during the dive, it is able to transit back into autorotation in an average duration of 327 ms. Semioutdoor experiments were used to investigate the bidirectional transitions and verify the glide angle (28.9 $^{\circ }$), which is much improved from the previous prototype (SAW+, 58.4 $^{\circ }$). Lastly, as a demonstration of a real-life deployment scenario and environmental conditions, the prototypes were dropped from a fixed-wing unmanned aerial vehicle at a suburban test site. [ABSTRACT FROM AUTHOR]

Insect-sized ($\sim$ 100 mg) aerial robots have advantages over larger robots because of their small size, low weight, and low materials cost. Previous iterations have demonstrated controlled flight but were difficult to fabricate because they consisted of many separate parts assembled together and were also unable to perform locomotion modes besides flight. This article presents a new design of a 74-mg flapping-wing robot that dramatically reduces the number of parts and simplifies fabrication. The robot also has a lower center of mass, which allows the robot to additionally land without the need for long legs, even in case of unstable flight. We also show that the new design allows for wing-driven ground and air–water interfacial locomotion, improving the versatility of the robot. During surface ambulation, forward thrust is generated by increasing the speed of the upstroke relative to the downstroke of the flapping wings. Adjusting relative wing stroke amplitudes also allows for steering. The ability to land and subsequently move along the ground first presented here allows the robot to negotiate extremely confined spaces and underneath obstacles. We present results demonstrating these capabilities, as well as hovering flight and controlled landing. [ABSTRACT FROM AUTHOR]

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1 for the community. [ABSTRACT FROM AUTHOR]

This paper proposes rigid-body modelling and identification procedures for long-reach dual-arm manipulators in a cable-suspended pendulum configuration. The proposed model relies on a virtually constrained open kinematic chain and lends itself to be simulated through the most commonly used robotic simulators without explicitly account for the cables constraints and flexibility. Moreover, a dynamic parameters identification procedure is devised to improve the simulation model fidelity and reduce the sim-to-real gap for controllers deployment. We show the capability of our model to handle different cable configurations and suspension mechanisms by customising it for two representative cable-suspended dual-arm manipulation systems: the LiCAS arms suspended by a drone and the CRANEbot system, featuring two Pilz arms suspended by a crane. The identified dynamic models are validated by comparing their evolution with data acquired from the real systems showing a high (between 91.3% to 99.4%) correlation of the response signals. In a comparison performed with baseline pendulum models, our model increases the simulation accuracy from 64.4% to 85.9%. The simulation environment and the related controllers are released as open-source code. • Cable-suspended long-reach manipulators perform hazardous inspection and maintenance. • Accurate simulation and control development for cable-suspended robots is crucial. • Simplified dynamic model can be readily integratable into standard simulators. • Identification procedure to reduce sim-to-real gap and develop model-based controls. [ABSTRACT FROM AUTHOR]

Recently, there have been many advances in the algorithms required for autonomous navigation in unknown environments, such as mapping, collision avoidance, trajectory planning, and motion control. These components have been integrated into drones with high‐end computers and graphics processors. However, further development is required to enable compute‐constrained platforms with such autonomous navigation capabilities. To address this issue, in this paper, we present an autonomous navigation framework for reaching a goal in unknown three‐dimensional cluttered environments. The framework consists of three main components. The first component is a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. The second component is a stochastic approach to generate a path to a given goal, taking into account the field of view constraints on the space that is assumed to be safe for navigation. The third method is a fast algorithm for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty, and disturbances. We provide a qualitative and quantitative comparison with existing reaching a goal and exploration methods, showing the superior performance of our approach. Additionally, we present indoors and outdoors experiments using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation, in the most computational constrained platform, of autonomous navigation in unknown cluttered environments demonstrated to date. Open source code is available at: https://github.com/IntelLabs/autonomousmavs. The video of the experimental results can be found in https://youtu.be/79IFfQfvXLE. [ABSTRACT FROM AUTHOR]

Performance of global navigation satellite systems (GNSSs) mounted on aerial platforms could be degraded by the presence of jamming or spoofing threats. Detection of jamming and spoofing is essential considering practical applications of satellite navigation in passenger aircrafts, unmanned aerial vehicles (UAVs), helicopters and fighters. Different algorithms and methods have been proposed for detection of these threats; however, their usage has many limitations because of their demanding weight, size and computational complexity, when embedded on aerial systems. In this study, the authors develop a theoretical framework to detect the presence of the threat of UAVs. The idea is based on the fact that, due to the UAV motion, the samples of received signal power from a fixed threat and from a GNSS satellite have different empirical probability density functions. Moreover, by using two antennas (an omnidirectional and a down‐tilted‐directional), they introduce a new method to distinguish between aerial and ground‐based threats. The proposed algorithms have a low‐computational burden and can consider the fading loss as well. Simulation results show the superior performance of the proposed methods, in terms of detection and false alarm probability, compared to the existing methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) carrying cable-suspended load is a basic method for load transportation. Tracking a given trajectory without the global positioning system or a prior environmental map with limited computation power and limited sensory is a crucial problem. In this paper, a novel paradigm/structure combining an adaptive state estimator and a trajectory tracking controller for the unmanned aerial vehicle with a cable-suspended load system is designed. An advantage of this work is the adaptive method to estimate the system state and environmental feature positions at the same time. The asymptotic convergence of our system is theoretically proved by the Lyapunov theory. The visual–inertial estimation algorithm we propose has lower computational consumption than the optimization-based methods and provides high-precision, low latency state estimation for the system executing aggressive flight. Several simulations are designed and carried out within an ODE-based simulator to verify the proposed method. Comparisons with other visual–inertial-based algorithms are also carried out in the simulator. Simulation results demonstrate the proposed algorithm's convergence, robustness, and efficiency. [ABSTRACT FROM AUTHOR]

The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.

This paper presents the design, modeling and control of a fully actuated aerial robot for infrastructure contact inspection as well as its localization system. Health assessment of transport infrastructure involves measurements with sensors in contact with the bridge and tunnel surfaces and the installation of monitoring sensing devices at specific points. The design of the aerial robot presented in the paper includes a 3DoF lightweight arm with a sensorized passive joint which can measure the contact force to regulate the force applied with the sensor on the structure. The aerial platform has been designed with tilted propellers to be fully actuated, achieving independent attitude and position control. It also mounts a “docking gear” to establish full contact with the infrastructure during the inspection, minimizing the measurement errors derived from the motion of the aerial platform and allowing full contact with the surface regardless of its condition (smooth, rough, ...). The localization system of the aerial robot uses multi-sensor fusion of the measurements of a topographic laser sensor on the ground and a tracking camera and inertial sensors on-board the aerial robot, to be able to fly under the bridge deck or close to the bridge pillars where GNSS satellite signals are not available. The paper also presents the modeling and control of the aerial robot. Validation experiments of the localization system and the control system, and with the aerial robot inspecting a real bridge are also included.

This paper presents the design, modeling and control of a fully actuated aerial robot for infrastructure contact inspection as well as its localization system. Health assessment of transport infrastructure involves measurements with sensors in contact with the bridge and tunnel surfaces and the installation of monitoring sensing devices at specific points. The design of the aerial robot presented in the paper includes a 3DoF lightweight arm with a sensorized passive joint which can measure the contact force to regulate the force applied with the sensor on the structure. The aerial platform has been designed with tilted propellers to be fully actuated, achieving independent attitude and position control. It also mounts a "docking gear" to establish full contact with the infrastructure during the inspection, minimizing the measurement errors derived from the motion of the aerial platform and allowing full contact with the surface regardless of its condition (smooth, rough,...). The localization system of the aerial robot uses multi-sensor fusion of the measurements of a topographic laser sensor on the ground and a tracking camera and inertial sensors on-board the aerial robot, to be able to fly under the bridge deck or close to the bridge pillars where GNSS satellite signals are not available. The paper also presents the modeling and control of the aerial robot. Validation experiments of the localization system and the control system, and with the aerial robot inspecting a real bridge are also included. [ABSTRACT FROM AUTHOR]