Untethered flight of an insect-sized flapping-wing microscale aerial vehicle.

In this letter, we present the untethered flight of an at-scale tailless hummingbird robot with independently controlled wings. It represents the first untethered stable flight of a two actuator powered bio-inspired Flapping Wing Micro Air Vehicle (FWMAV) in both indoor and outdoor environment. The untethered flight of such FWMAVs is a challenging task due to stringent payload limitation from severe underactuation and power efficiency challenge caused by motor reciprocating motion. In this work, we present the detailed modeling, optimization, and system integration of onboard power, actuation, sensing, and flight control to address these unique challenges of such FWMAV during untethered flight. We performed untethered flight experiments in both indoor and outdoor environment and demonstrate sustained stable flight of the robot.

It is challenging for microrobots to fly using onboard energy due to the relatively heavy power supplies and inefficient actuators that are limited to a small size (mass less than 1000 mg and wingspan less than 100 mm). Inspired by the movement of jellyfish in nature (Physophora hydrostatica), herein, a microscale aerial vehicle FlyJelly is introduced, powered by onboard energy to achieve untethered flight. FlyJelly uses a balloon filled with helium to overcome self‐gravity and an actuator powered by electrostatic power to generate thrust. The balloon, heat sealed with two gold‐covered Mylar films, weighs 95.56 mg, provides 257.9 mg of buoyancy when filled with helium, and stores 19.457 × 10−3 J of energy when powered by 2400 V of direct current. The electrostatic actuator is composed of multiflapping units arranged radially and symmetrically, consuming only 0.3370 mW of power but generating a thrust of 0.2271 mN to drive the vehicle for flight. Benefiting from a design of distributed propulsion, FlyJelly is highly reliable and continues to work well even after the actuator is damaged. The milligram‐level weight, untethered flight with onboard energy, and high reliability make the prototype suitable for development as a long‐term remote exploration and search‐and‐rescue mission.

Historically, helicopters with four rotors (quad-rotors) have been very uncommon, mainly due to the fact that most of the common payloads could be lifted using one or two rotors. However, the quad-rotor possesses some special characteristics that make it attractive. One would be the superior payload capacity. Two, is the simplicity of the control system: the absence of complex rotor mechanisms and just by independently adjusting the speed of each rotor it is possible to control both the attitude and the horizontal/vertical motion. This system is particularly suitable for small unmanned aerial vehicles (UAV), because it reduces the complex mechanism of the rotors (saving volume and weight) and simplifies the control algorithms required for stable, untethered flight. Although much progress has been made in the field of quad-rotor UAVs, it is still a great challenge to build a quad-rotor capable of fully autonomous flight. Before the decision of appropriate control algorithms, it is essential to have complete understanding of quad-rotor dynamics and equation of motion. This technical paper presents a detailed quad rotor model design as well as the detailed algorithm for the control system. An ANSYS flow simulation was carried out on the modular structure of the quad rotor. Different sensors were integrated with the control system to improve the stability and to reduce the pilot’s workload. Its capabilities will be enhanced with semi-autonomous and autonomous functions that were implemented with the usage of GPS.

Evaluating How Demand Side Resources Affect the Environmental and Economic Performance of Energy Systems

Limited flight duration is a considerable obstacle to the widespread application of micro aerial vehicles (MAVs)1-3, especially for ultralightweight MAVs weighing less than 10 g, which, in general, have a flight endurance of no more than 10 min (refs. 1,4). Sunlight power5-7 is a potential alternative to improve the endurance of ultralight MAVs, but owing to the restricted payload capacity of the vehicle and low lift-to-power efficiency of traditional propulsion systems, previous studies have not achieved untethered sustained flight of MAVs fully powered by natural sunlight8,9. Here, to address these challenges, we introduce the CoulombFly, an electrostatic flyer consisting of an electrostatic-driven propulsion system with a high lift-to-power efficiency of 30.7 g W-1 and an ultralight kilovolt power system with a low power consumption of 0.568 W, to realize solar-powered sustained flight of an MAV under natural sunlight conditions (920W m-2). The vehicle's total mass is only 4.21 g, within 1/600 of the existing lightest sunlight-powered aerial vehicle6.

This paper presents the mathematical modeling of flapping flight inch-size micro aerial vehicles (MAVs), namely micromechanical flying insects (MFIs). The target robotic insects are electromechanical devices propelled by a pair of independent flapping wings to achieve sustained autonomous flight, thereby mimicking real insects. In this paper, we describe the system dynamic models which include several elements that are substantially different from those present in fixed or rotary wing MAVs. These models include the wing-thorax dynamics, the flapping flight aerodynamics at a low Reynolds number regime, the body dynamics, and the biomimetic sensory system consisting of ocelli, halteres, magnetic compass, and optical flow sensors. The mathematical models are developed based on biological principles, analytical models, and experimental data. They are presented in the Virtual Insect Flight Simulator (VIFS) and are integrated together to give a realistic simulation for MFI and insect flight. VIFS is a software tool intended for modeling flapping flight mechanisms and for testing and evaluating the performance of different flight control algorithms

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper.

Additive manufacturing in construction typically consists of ground-based platforms. Introducing aerial capabilities offers scope to create or repair structures in dangerous or elevated locations. The Aerial Additive Manufacturing (AAM) project has developed a pioneering approach using Unmanned Aerial Vehicles (UAV, ‘drones’) to deposit material during self-powered, autonomous, untethered flight. This study investigates high and low-density foams autonomously deposited as structural and insulation materials. Drilling resistance, mechanical, thermal and microscopy tests investigate density variation, interfacial integrity and thermal stability. Autonomous deposition is demonstrated using a flying UAV and robotic arm. Results reveal dense material at interfaces and directionally dependent cell expansion during foaming. Cured interfacial regions are vulnerable to loading parallel to interfaces but resistant to perpendicular loading. Mitigation of trajectory printing errors caused by UAV flight disturbance is demonstrated by a stabilising end effector, with trajectory errors ≤10 mm. AAM provides a significant development towards on-site automation in construction. Highlights Aerial Additive Manufacturing (AAM) releases additive manufacturing (AM) for construction applications from ground-based and tethered restraints. Multiple self-powered flying Unmanned Aerial Vehicles (UAV) can deposit layers of polyurethane foam in planned trajectories. High-density polyurethane foam and low-density foam can be suitable for structural and insulating layers, respectively. Laboratory tests, including drilling resistance, demonstrate the high-density of interfacial boundary regions in relation to material located away from a boundary. The challenges of reducing lateral deformation of extruded material are evaluated, and improved flight stabilisation provided by an end effector keeping trajectory errors within 10 mm is demonstrated.

This paper proposes modeling and solution methods for aerial vehicle jamming systems, focusing on jamming cloud motion patterns, aerial vehicle occlusion detection, and multi-jamming cloud collaborative optimization. First, a jamming cloud motion model is established across three time intervals. The vertical foot method determines the closest point between the jamming cloud center and the line connecting the aerial vehicle and target. Combined with the discrete time point method, time discretization, distance calculation, and decision functions are employed to construct the occlusion duration solution. Second, addressing the parameter optimization problem, a genetic algorithm is employed. Chromosomes containing optimization variables are constructed, with the objective function serving as the fitness function, to solve for optimized parameters and corresponding occlusion durations. Finally, the model is extended to multi-jamming-cloud scenarios. Based on the minimum distance criterion, it determines whether the flight vehicle is obscured. Through inner-outer network searches, parameter separation, variable dimension reduction, and combined with a greedy algorithm, it iteratively optimizes the deployment and detonation times of jamming munitions, targeting additional obstruction duration. This model accurately depicts the interaction process between jamming clouds and flight vehicles. The algorithms employed adapt to both single- and multi-jamming-cloud scenarios, effectively enhancing jamming effectiveness and parameter optimization efficiency.

Unmanned aerial vehicles are rapidly evolving within the field of robotics. However, their performance is often limited by payload capacity, operational time, and robustness to impact and collision. These limitations of aerial vehicles become more acute for missions in challenging environments such as subterranean structures which may require extended autonomous operation in confined spaces. While software solutions for aerial robots are developing rapidly, improvements to hardware are critical to applying advanced planners and algorithms in large and dangerous environments where the short range and high susceptibility to collisions of most modern aerial robots make applications in realistic subterranean missions infeasible. To provide such hardware capabilities, one needs to design and implement a hardware solution that takes into the account the Size, Weight, and Power (SWaP) constraints. This work focuses on providing a robust and versatile hybrid platform that improves payload capacity, operation time, endurance, and versatility. The Bi-modal Aerial and Terrestrial hybrid vehicle (BAXTER) is a solution that provides two modes of operation, aerial and terrestrial. BAXTER employs two novel hardware mechanisms: the M-Suspension and the Decoupled Transmission which together provide resilience during landing and crashes and efficient terrestrial operation. Extensive flight tests were conducted to characterize the vehicle’s capabilities, including robustness and endurance. Additionally, we propose Agile Mode Transfer (AMT), a transition from aerial to terrestrial operation that seeks to minimize impulses during impact to the ground which is a quick and simple transition process that exploits BAXTER’s resilience to impact.

The robustness testing method of the traditional multi MIMO photovoltaic control generation structure, when the robustness of the detection organization interference, does not score the robust evaluation index of the photovoltaic control generation system, and the weight stratification of the measured sample weight is not clear, which primes to the great error of the test results, and suggests a new technique of robustness testing under the interference of the photovoltaic system. The Internet of Things (IoT) protection receives significant attention as the little. The capacity limits and difficulty characteristics of many IoT devices hinder the usage of traditional devices. A summary of recent study activities on alternative research is given in this report. Approaches to protect the physical layer of IoT wireless communications, precisely the main approaches Key Generation and Physical Layer Encryption Issues. It is necessary to enforce these systems and are Lightweight, thus offering realistic solutions to deliver reliable wireless IoT protection. The weight vector of the adaptive weighted BP neural network is optimized by the information and information change, and the robustness test of the photovoltaic control generation system is realized through the robust test model of the optical fiber power system based on the BP neural network. The experimental results show that the planned technique can precisely detect the strength of PV system under interference, and has the advantage of high efficiency

The paper presents a qualitatively new approach to terminal guidance at the final trajectory section for the surface-to-surface class aerial vehicles. The proposed structure of the adaptive control system for an aerial vehicle is based on the multi-step terminal guidance algorithm. Adaptive corrections to the control coefficients were calculated using the developed method for identifying the wind disturbances based on the machine learning models. The work describes technique to form an intelligent algorithm for identifying intensity and direction of the wind load acting on the aerial vehicle in flight. Options of the machine learning models used in the guidance system intelligent block were investigated; their operation results are presented; and the comparative analysis has been carried out. The adaptive guidance system operation procedure is demonstrated on a typical model of the aerial vehicle flying in the atmosphere and targeting a fixed object. Numerical simulation results are presented, and possibility of using such an algorithm and implementing the described system are demonstrated.

Unmanned aerial vehicles, commonly known as drones, are increasingly used in ecological management, conservation and research. Numerous reviews on drones tout almost unlimited potential within the wildlife sciences as they open up inaccessible habitats to observation. However, the influence of drones on the animals themselves is far less understood, and impact studies to construct protocols for best practices are urgently needed to minimise the potential for stress on target species. The impact of a quadcopter drone’s approach speed, angle of approach and initial starting altitude was tested on the behavioural responses of African elephants (Loxodonta africana), along with sustained speed and flight pattern. Seventy-nine approach flights and 70 presence flights were conducted. The speed and angle of approach significantly impacted the success of a flight, but neither speed nor flight pattern had any measurable impact on elephants’ behaviour during sustained flights. It is recommended that drones be launched at a distance of 100 m from an elephant or a herd of elephants, ascending to a height of 50 m by using an approach speed of 2m/s and an approach angle of 45 ° or less to successfully contact elephant targets. Conservation implications: This study aimed to provide a significant step towards the ethical use of drones in wildlife research. Further research is required to investigate the impacts of drones on other taxa. Physiological responses to drones, for example, would determine if physiological stress responses unlinked to behavioural indicators are of concern in elephants.

Modelling the impact of market imperfections on farm household investment in stand-alone solar PV systems

Unmanned ground vehicles and aerial vehicles can utilize their complementary features to address each other's inabilities like limited payload capacity, endurance, agility and field of view and thereby improving the mission efficiency. This paper presents an in-house developed ground and a micro aerial vehicle system performing a coordinated mission. The aerial vehicle is equipped with a stereo image sensor using which the environment around the ground vehicle is reconstructed in the form of an elevation map. This is then processed to get 2D traversability map based on which the ground vehicle performs path planning for a specified goal. The aerial robot also gives a sparse feature map which is used by the ground vehicle for initialization of its pose in the map. A pure pursuit controller for path following is implemented for both aerial and ground vehicle. Most of the proposed concepts are implemented and demonstrated on a real platform. This work also aims to build a base for developing in-house controllers and localization filters that can be used for real-time testing thereby decreasing the time for development.