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When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.

In numerous industries such as aerospace and energy, components must perform under significant extreme environments. This imposes stringent requirements on the accuracy with which these components are manufactured and assembled. One such example is the positional tolerance of drilled holes for close clearance applications, as seen in the “EN3201:2008 Aerospace Series – Holes for metric fasteners” standard. In such applications, the drilled holes must be accurate to within ±0.1 mm. Traditionally, this required the use of Computerised Numerical Control (CNC) systems to achieve such tight tolerances. However, with the increasing popularity of robotic arms in machining applications, as well as their relatively lower cost compared to CNC systems, it becomes necessary to assess the ability of robotic arms to achieve such tolerances. This review paper discusses the sources of errors in robotic arm drilling and reviews the current techniques for improving its accuracy.

Robotic arms are widely known to fall short in achieving the tolerances required when it comes to the metal machining industry, especially for the aerospace sector. Broadly speaking, two of the main reasons for that are a lack of stiffness and a lack of accuracy. Robotic arm manufacturers have responded to the lack of stiffness challenge by producing bigger robots, capable of holding high payloads (e.g., Fanuc M-2000iA/2300) or symmetric robots (e.g., ABB IRB6660). Previous research proved that depending on the application and the material being machined, lack of stiffness will still be an issue, even for structurally bigger robotic arms, due to their serial nature. The accuracy issue has been addressed to a certain extent by using secondary encoders on the robotic arm joints. The encoder enhanced robotic arm solutions tend to be expensive and prior knowledge proves that there are still limitations when it comes to achieved accuracy.

Heavy Commercial Road Vehicles (HCRVs) may be more susceptible to rollover incidents due to their higher centre of gravity position than passenger vehicles, and rollover is one of the significant causes of HCRV accidents. Therefore, variation in vehicle roll behaviour becomes crucial to the safety of an HCRV. Toe misalignment is a commonly observed phenomenon in HCRVs, and studying its impact on roll behaviour is important. In this study, the impact of the symmetric toe and thrust misalignment on the roll behaviour of an HCRV is analysed using IPG TruckMaker®, a vehicle dynamics simulation software. A ramp steer manoeuvre was used for the simulations, and the toe misalignment on a wheel was chosen from the range [-0.21°, 0.21°]. Variation in roll behaviour was quantified using the steering wheel angle at which one-wheel lift-off (OWL) occurred (SWAL).

Effective smart cockpit interaction design can address the specific needs of children, offering ample entertainment and educational resources to enhance their on-board experience. Currently, substantial attention is focused on smart cockpit design to enrich the overall travel engagement for children. Recognizing the contrasts between children and adults in areas such as physical health, cognitive development, and emotional psychology, it becomes imperative to meticulously customize the design and optimization processes to cater explicitly to their individual requirements. However, a noticeable gap persists in both research methodologies and product offerings within this domain.

Conventional trajectory planning methods encounter various challenges: Inability to better distinguish different types of vehicles, and failure to consider the difference between perceived threats or risks during asymmetric and symmetric interactions for autonomous vehicles. To solve these issues, the insufficiency of the traditional risk-field model is analyzed, and an asymmetric aggressiveness model is investigated in this study, which quantifies the suffered aggressiveness of vehicles. Then, the asymmetric aggressiveness model and the static potential risk field describing the road structure are used as the control objectives of the optimal controller to avoid collisions. Furthermore, a three-degree-of-freedom vehicle dynamics model is constructed, and the optimal feasible trajectory is planned by using the model predictive control algorithm.

Ultrafine particles, in particular solid sub-100 nm particles pose high risks to human health due to their high lung deposition efficiency, translocation to all organs including the brain and their harmful chemical composition; due to dense traffic, the population in urban environments is exposed to high concentrations of those toxic air contaminants, despite these facts, they are still widely neglected. Therefore, the EU-Commission set up a program for clean and competitive solutions for different problem areas which are regarded to be hotspots of such particles. HORIZON AeroSolfd is an EU project, co-funded by Switzerland that will deliver affordable, adaptable, and sustainable retrofit solutions to reduce exhaust tailpipe emissions from petrol engines, brake emissions and pollution in semi-closed environments.

The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts.

For nearly a century, ice build-up on aircraft surfaces has presented a safety concern for the aviation industry. Pilot observations of visible moisture and temperature has been used a primary means to detect conditions conducive to ice accretion on aircraft critical surfaces. To help relieve flight crew workload and improve aircraft safety, various ice detection systems have been developed. Some ice detection systems have been successfully certified as the primary means of detecting ice, negating the need for the flight crew to actively monitor for icing conditions. To achieve certification as a Primary ice detection system requires detailed substantiation of ice detector performance over the full range of icing conditions and aircraft flight conditions. Some notable events in the aviation industry have highlighted certain areas of the icing envelope that require special attention.

Distinct atmospheric conditions containing supercooled large droplets (SLD) have been identified as cause of severe accidents over the last decades as existing countermeasures even on modern aircraft are not necessarily effective against SLD-ice. Therefore, the detection of such conditions is crucial and required for future transport aircraft certification. However, the reliable detection is a very challenging task. The EU funded Horizon 2020 project SENS4ICE targets this gap with new ice detection approaches and innovative sensor hybridization. The indirect ice detection methodology presented herein is key to this approach and based on the changes of airplane flight characteristics under icing influence. A performance-based approach is chosen detecting an abnormal flight performance throughout the normal operational flight. It is solely based on a priori knowledge about the aircraft characteristic and the current measurable flight state.

Extremely uncomfortable levels of bounce and pitch vibrations are produced when a CV moves over uneven terrain. The present study was carried out to ascertain the vibrational response at the driver’s, commander’s and trooper’s seats. A 23 -degrees of freedom (DOF) lumped parameter 3-D model of a combined CV and human body was made. The vehicle had 15 DOF corresponding to the bounce, pitch and roll of the hull (sprung mass) and bounce motions of the 12 wheel stations (unsprung masses) on either side. The human body was idealized as having 8 DOF corresponding to bounce motions of the pelvis, abdomen, diaphragm, thorax, torso, back and head. The seat was also assigned a bounce DOF. The lumped masses of the body parts were distributed and connected by springs. The differential equations of motion for the linear rigid body model were formulated and the natural frequencies of different parts of the human body and the military tank were determined by eigenvalue analysis using MATLAB.

It is estimated that the share of autonomous vehicles in the market will reach an important point between 2050s and 2060s. Some major benefits of autonomy in ground vehicles can be regarded as reducing traffic, saving fuel and reducing emissions. Accordingly, it is anticipated that autonomous vehicles (AVs) will prevent driver error from happening, which is the primary cause of 90% of traffic accidents. However, it is a prerequisite that the AVs are accepted by the public, and be used regularly in daily life. AVs obliges everyone to be a passenger, thereby occupants will lose authority on the vehicle and have to deal with non-driving tasks during an automated ride. This will increase the lack of situational awareness, leading occupants to be more sensitive to motion sickness, where the major reasons of motion sickness are conflict between vestibular and visual senses, lack of control, unable to predict the direction of movement.

Significant growth of Unmanned Aerial Vehicles (UAV) has unlocked many services and applications opportunities in the healthcare sector. Aerial transportation of medical cargo delivery can be an effective and alternative way to ground-based transport systems in times of emergency. To improve the security and the trust of such aerial transportation systems, Blockchain can be used as a potential technology to manage, operate and monitor the entire process. In this paper, we present a blockchain network solution based on Ethereum for the transportation of medical cargo such as blood, medicines, vaccines, etc. The smart contract solution developed in solidity language was tested using the Truffle program. Ganache blockchain test network was employed to host the blockchain network and test the operation of the proposed blockchain model. The suitability of the model is validated in real-time using a UAV and all the flight data are captured and uploaded into the blockchain.

The extent of automation and autonomy used in general aviation (GA) has been accelerating dramatically. This has huge potential benefits for safety given that 75% of accidents in personal and on-demand GA are due to pilot error. However, an approach to certifying autonomous systems that relies on reversionary modes limits their potential to improve safety. Placing a human pilot in a situation where they are suddenly tasked with flying an airplane in a failed situation, often without sufficient situational awareness, is overly demanding. This, coupled with advancing technology that may not align with a deterministic certification paradigm, creates an opportunity for new approaches to certifying autonomous and highly automated aircraft systems.

In this paper, a closed loop path planning and tracking control approach of collision avoidance for autonomous vehicle is proposed. The two-level model predictive control (MPC) is proposed for the path planning and tracking. The upper-level MPC is designed based on the simple vehicle kinematic model to calculate the collision-free trajectory and the potential field method is adopted to evaluate the collision risk and generate the cost function of the optimization problem. The lower-level MPC is the trajectory-tracking controller based on the vehicle dynamics model that calculates the desired control inputs. Finally the control inputs are distributed to steering wheel angle and motor torque via optimal control vectoring algorithm. Test cases are established on the Simulink/CarSim platform to evaluate the performance of the controller.

The current drone market holds a lot of low-cost drones that can be controlled either remotely or autonomously. The command controlling signals can be deployed by using different methods and techniques to enhance the capabilities and missions of the drone. The microcontroller is usually used as the drone brain. Motion control and virtual reality (VR) techniques allow the user new possibilities for a more dynamic control experience. The goal of the present work is to design, develop, and deliver a working prototype of an enhanced VR motion-controlled drone. The developed prototype is an integrated system that includes an off-the-shelf drone, accelerometer and gyroscope unit, flex sensor, gloves, digital potentiometer, VR headset, and microcontroller. The flex sensor attached to the hand gloves allows a fully autonomous control of the drone using the movement of the hand. Hence, the gloves will allow for a more dynamic control of the drone.

With the increasing adoption of electric vehicles in India, autos are also getting in the electrification race with lighter lithium-ion batteries and motor replacing the bulkier engine and transmission. This trend has led to a lighter vehicle which in-turn gives better mileage figures but at the loss of dynamic stability of the vehicle making them very unsafe. The current auto-rickshaws are using delta configuration that is more prone to the rollover while cornering. The three-wheeled configuration vehicle is less dynamically stable than the normal four-wheeled configurations. While working on prototype vehicle for Shell Eco-Marathon Asia [7] pro and cons for both configurations for a three-wheeled vehicle were considered and tadpole configuration was found to be more stable and better than current delta configuration.

Unmanned aircraft systems (UAS), commonly known as drones, are part of a new and budding industry in the United States. Economic and public benefits associated with UAS use across multiple commercial sectors are driving new regulations which alter the stringent laws currently restricting UAS flights over people. As new regulations are enacted and more UAS populate the national airspace, there is a need to both understand and quantify the risk associated with UAS impacts with the uninvolved public. The purpose of this study was to investigate the biomechanical response and injury outcomes of Post Mortem Human Surrogates (PMHS) subjected to UAS head impacts. For this work, PMHS were tested with differing UAS vehicles at multiple impact angles, locations and speeds. Using a custom designed launching device, UAS vehicles were accelerated into the frontal, parietal, or vertex portions of subjects’ craniums at speeds up to 22 m/s.