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In this study, nonlinear crash analyses have been conducted for the skid landing gear of helicopter. The realistic configuration of skid landing gear system is considered. Detailed three-dimensional finite element model with variable thickness and material nonlinearity is constructed for required impact design conditions. Advanced computational approach is used to conduct nonlinear transient impact dynamic analyses for different collision models. Characteristics of impact dynamic responses due to the ground crash are practically investigated in detail. It is also shown that the exact consideration of friction effect is very important to accurately predict the crash behavior of the skid type landing gear system. Finally, two typical landing conditions are analyzed and correlated with drop test results.

A parallel Navier-Stokes solver based on dynamic overset unstructured grids method is presented to simulate the unsteady turbulent flow field around helicopter in forward flight. The grid method has the advantages of unstructured grid and Chimera grid and is suitable to deal with multiple bodies in relatively moving. Unsteady Navier-Stokes equations are solved on overset unstructured grids by an explicit dual time-stepping, finite volume method. Preconditioning method applied to inner iteration of the dual-time stepping is used to speed up the convergence of numerical simulation. The Spalart-Allmaras one-equation turbulence model is used to evaluate the turbulent viscosity. Parallel computation is based on the dynamic domain decomposition method in overset unstructured grids system at each physical time step. A generic helicopter Robin with a four-blade rotor in forward flight is considered to validate the method presented in this paper. Numerical simulation results show that the parallel dynamic overset unstructured grids method is very efficient for the simulation of helicopter flow field and the results are reliable.

In this paper, we propose an extended helicopter's rescuing model to explore the effects of multiple rescuing points on each helicopter's flying behavior during the whole rescuing process from the numerical perspective. The numerical results show that the proposed model can describe the qualitative effects of multiple rescuing points on each helicopter's speed, running trail and safe region.

A synopsis of a multidisciplinary research initiative focused on critical strategies for the genuine independent flight of tiny, vertical flying take-off (VTOL) unmanned aerial vehicles (UAVs). The research activities are the flight testbed, a simulation and test environment, and integrated components for onboard navigation, perception, design, and control. The necessity to create an unmanned helicopter system in different new civil applications cannot be overlooked. A highly reliable model may be used in the design, analysis, and implementation. The helicopter is fitted with a reference system for flight test data measurement and recording attitude heading reference system (AHRS) and the accompanying data storage modules. Recently, artificial intelligence-based deep learning (DL) has demonstrated excellent outcomes for a wide range of robotic activities in the areas of perception, planning, location, and management. Its remarkable skills to learn from complex data obtained in actual surroundings make it appropriate for many autonomous robotic applications. At the same time, UHS is currently widely utilized in various civil tasks in security, cinematography, disaster assistance, package delivery, or warehouse management (Unmanned Helicopter System). This paper conducted detailed work on current applications and the most significant advances and their performance and limits for the DL-UHS method. Furthermore, the essential strategies for deep learning are explained in depth — finally, discussing the principal hurdles of applying deep learning for UHS solutions. The proposed DL-UHS enhance outcome to evaluate the control strategies for the unmanned helicopter to achieve the low signal to noise error ratio of 31.3%, the error rate of 33.6%, the high-performance ratio of 91.4%, enhance accurate path planning 97.5%, prediction ratio of 96.3%, less trajectory cost ratio of 17.8% and increased safety tracking rate 93.6% when compared to other popular methods.

This paper presents the design of a relative low-cost and more compatible autonomous helicopter system using HIROBO 50 scale as an experimental platform. Because of the limit of helicopter payload, we choose the MP2128 Autopilot and a number of sensors to build the system and the weight of instrumentation is about 500 g, much less than the payload capability of model helicopter. Thus it is feasible to design the binocular stereo-camera system to achieve full autonomous flight and the whole weight (include power) of instrumentation is about 1500 g. After getting the model of the helicopter using the subspace model identification (SMI) algorithms, we present the structure of fuzzy PID controller.

Modeling and simulation are used to support a wide range of applications including design, analysis, operations research, test and evaluation, training, etc. Each application has different fidelity requirements and computational limitations. Consequently, a variety of models are required to support a single aircraft type throughout its life cycle. Validation and configuration management of this array of models is costly. FLIGHTLAB${}^{\text{TM}}$is a software tool that supports selective fidelity modeling and simulation to insure trace-ability and commonality between models, ranging from the comprehensive models required for design to the real time models required for training and hardware-in-the-loop testing. Providing a single tool with this range of modeling options greatly enhances the configuration management capability. Also once the highest fidelity, comprehensive, model is validated, it can be used to provide reference data for validation of simpler models, thereby expediting the validation process for all levels of modeling. This paper describes the real-time simulation capability of FLIGHTLAB${}^{\text{TM}}$models and their trace-ability to higher level FLIGHTLAB${}^{\text{TM}}$models.

The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. This work addresses the attitude and position estimation of a small-size unmanned helicopter tethered to a moving platform using a multi-sensor data fusion algorithm based on a numerically efficient sigma-point Kalman filter implementation. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using information from additional sensors like magnetometer, barometric altimeter, LIDAR altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on the platform during the system operation, the helicopter position is estimated relative to the anchor point. Although this configuration avoids the need of a GPS, a standard operation mode for estimation of the absolute position (the position relative to the inertial reference frame) incorporating corrections with the GPS position and velocity measurements, is also implemented in order to highlight the benefits of the proposed tethered setup. The filter performance is evaluated in simulations.