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When darkness falls, storms rage, fog settles, or lights fail, pilots are forced to make \"instrument landings,\" relying on technology and training to guide them through typically the most dangerous part of any flight. In this original study, Erik M. Conway recounts one of the most important stories in aviation history: the evolution of aircraft landing aids that make landing safe and routine in almost all weather conditions. Discussing technologies such as the Loth leader-cable system, the American National Bureau of Standards system, and, its descendants, the Instrument Landing System, the MIT-Army-Sperry Gyroscope microwave blind landing system, and the MIT Radiation Lab's radar-based Ground Controlled Approach system, Conway interweaves technological change, training innovation, and pilots' experiences to examine the evolution of blind landing technologies. He shows how systems originally intended to produce routine, all-weather blind landings gradually developed into routine instrument-guided approaches. Even so, after two decades of development and experience, pilots still did not want to place the most critical phase of flight, the landing, entirely in technology's invisible hand. By the end of World War II, the very concept of landing blind therefore had disappeared from the trade literature, a victim of human limitations.

Aircraft landing gear assemblies comprise of various subsystems working in unison to enable functionalities such as taxiing, take-off and landing. As development cycles and prototyping iterations begin to shorten, it is important to develop and improve practical methodologies to meet certain design metrics. This paper presents an efficient methodology that applies high-fidelity multi-disciplinary design optimization techniques to commercial landing gear assemblies, for weight, cost, and structural performance by considering both structural and dynamic behaviours. First, a simplified landing gear assembly model was created to complement with an accurate slave link subassembly, generated based of drawings supplied from the industrial partner, Safran Landing Systems. Second, a Multi-Body Dynamic (MBD) analysis was performed using realistic input motion signals to replicate the dynamic behaviour of the physical system. The third stage involved performing topology optimization with results from the MBD analysis; this can be achieved through the utilization of the Equivalent Static Load Method (ESLM). Lastly, topology results were generated and design interpretation was performed to generate two designs of different approaches. The first design involved trying to closely match the topology results and resulted in a design with an overall weight savings of 67%, peak stress increase of 74%, and no apparent cost savings due to complex features. The second design focused on manufacturability and achieved overall weight saving of 36%, peak stress increase of 6%, and an estimated 60% in cost savings.

In view of the certification for the CAT III autoland of Civil aircraft, an analysis method based on Monte Carlo simulation is proposed. First of all, the performance requirements of CAT III autoland of Civil aircraft are analyzed, and then the autoland model of aircraft with six degrees of freedom is established, and the guidance law, autoland control law and steady wind random model are designed, and then the quantitative analysis of the touchdown point, touchdown attitude angle and vertical speed are carried out by Monte Carlo random simulation. The simulation results show that the autoland system satisfy the CAT III Autoland design requirements under steady wind interference, and recognize the factors that have a great influence on the automatic landing performance and the limitations of the CAT III autoland system through Monte Carlo simulation, which provides reference for the subsequent airworthiness compliance verification of civil aircraft.

This paper presents the network-based modeling, validation and analysis of the nonlinear liquid spring damper model under vertical landing conditions of reusable launch vehicle. The impedance function of damper model is derived first. Then, its mechanical and hydraulic networks are newly established based on the hydro-mechanical analogy and network-based analysis. By comparing the networks between the corresponding symmetric and asymmetric structures, the meaning of each branch in the network is elucidated. After that, the validity of the network-based model for the liquid spring damper is confirmed by comparison against the experimentally verified nonlinear model in both frequency and time domain. The force and energy absorption characteristics of the damper model are further decomposed, and, specifically, the influence of the orifice area and orifice length on the attenuation performance is studied. The results show that the network-based model provides predictions consistent with those generated by the nonlinear model. The main discrepancy is attributed to the inaccuracy caused by the equivalent fluid bulk modulus. The network-based analysis indicates that the orifice area mainly influences the damping force in the network, which further affects the loads and efficiency of the damper. The orifice length mainly influences the inertia force in the network, which should be limited to a small value. The proposed novel interpretation of the damper models and responses under impact conditions constitutes a framework suitable for systematic design of typically highly nonlinear landing systems in reusable launch vehicles.

The ExoMars program is an ESA–Roscosmos cooperation with some NASA contributions. ExoMars consists of two missions, one in 2016 and one in 2018. The 2016 mission includes an orbiting satellite dedicated to the study of atmospheric trace gases to acquire information on possible on-going geological or biological processes, and a European entry, descent, and landing demonstrator module (EDM) to achieve a successful soft landing on Mars. The orbiter can also provide data communication services for all surface missions landing on Mars until the end of 2022. The 2018 mission is planned to deliver a 300-kg-class rover and an instrumented landing platform to the Martian surface using a landing system developed by Roscosmos. The 2018 mission is to pursue one of the most outstanding questions of our time by attempting to establish whether life ever existed, or is still present, on Mars today. The article gives an overview of the ExoMars program.

Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems.

Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive “band” information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model’s ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available.

Approach/Landing is the phase of most accidents occur to endanger civil aircraft safety. CAT III autoland operation can improve the safety and reliability of aircraft when approach and landing in bad weather conditions, and reduce pilot’s working loads, so CAT III autoland control law design is an important task in advanced flight control system research. However, the most of the related researches are requirement based rather and scenario based. Firstly, the article analyzes the Monte Carlo simulation requirements based on airworthiness requirements of auto-landing system and proposes a Monte Carlo simulation model scheme considering the uncertainty of aircraft dynamic model and the actuator model, the error of sensors and the disturbance of wind, then implements the simulation based on a desktop simulation platform. The simulation result shows that the designed auto-landing system can assure the safe and stable landing of the aircraft on the condition of all kinds of uncertainty, the touchdown point, the vertical landing speed and the attitude are all compliance with the expectation criteria, satisfying the requirement of airworthiness items.

Aircraft landing rollout process is a complex and high non-linearity process. The mutual effects of tire dynamics, aerodynamics and multi-body dynamics should be taken into considerations. It is difficult to be accurately expressed in mathematics. Aiming at obtaining a good directional control capability during aircraft autoland rollout, a comprehensive simulation system was constructed including all those three aspects above, and a fuzzy control method with intelligent weight function was adopted which is appropriate for systems difficult to be modeled exactly. In the paper’s work, the intelligent weight function was optimized by the power function for the first time. By adjusting the exponential k , the fuzzy control rules can be conveniently and reasonably optimized. Through a set of simulations without crosswind, the optimal k was obtained. Finally, the simulation tests under conditions of 20 kn crosswind with ±5° original drift angles were conducted with the system configured by the optimal k . Accroding to the results, the optimized fuzzy controller can give a good directional control performance.

Landing zone detection of autonomous aerial vehicles is crucial for locating suitable landing areas. Currently, landing zone localization predominantly relies on methods that use RGB cameras. These sensors offer the advantage of integration into the majority of autonomous vehicles. However, they lack depth perception, which can lead to the suggestion of non-viable landing zones, as they only assess an area using RGB information. They do not consider if the surface is irregular or accessible for a user (easily accessible to a person on foot). An alternative approach is to utilize 3D information extracted from depth images, but this introduces the challenge of correctly interpreting depth ambiguity. Motivated by the latter, we propose a methodology for 3D landing zone segmentation using a DNN-Superpixel approach. This methodology consists of three steps: First, the proposal involves clustering depth information using superpixels to segment, locate, and delimit zones within the scene. Second, we propose feature extraction from adjacent objects through a bounding box of the analyzed area. Finally, this methodology uses a Deep Neural Network (DNN) to segment a 3D area as landable or non-landable, considering its accessibility. The experimental results are feasible and promising. For example, the landing zone detection achieved an average recall of 0.953, meaning that this approach identified 95.3% of the pixels according to the ground truth. In addition, we have an average precision of 0.949, meaning that this approach segments 94.9% of the landing zones correctly.