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This SAE EDGE™ Research Report builds a comprehensive picture of the current state-of-the-art of human-robot applications, identifying key issues to unlock the technology’s potential. It brings together views of recognized thought leaders to understand and deconstruct the myths and realities of human- robot collaboration, and how it could eventually have the impact envisaged by many. Current thinking suggests that the emerging technology of human-robot collaboration provides an ideal solution, combining the flexibility and skill of human operators with the precision, repeatability, and reliability of robots. Yet, the topic tends to generate intense reactions ranging from a “brave new future” for aircraft manufacturing and assembly, to workers living in fear of a robot invasion and lost jobs. It is widely acknowledged that the application of robotics and automation in aerospace manufacturing is significantly lower than might be expected.

Transient force and surface pressure data has been measured on a range of simple geometric shapes in order to gain an understanding of the complex time dependent and separated flow around a vehicle when subjected to a crosswind. The experiments were carried out using the Cranfield University model crosswind facility. It is found that the leeward face is the dominant area of transient activity. Maximum and minimum peak yawing moments at gust entry and exit are compared

With the rapid growth in passenger transportation through aviation projected to continue into the future, it is incumbent on aerospace engineers to seek ways to reduce the negative impact of airliner operation on the environment. Key metrics to address include noise, fuel consumption, Carbon Dioxide and Nitrous Oxide emissions, and contrail formation. The research presented in this paper generates new aircraft trajectories to reduce these metrics, and compares them with typical scheduled airline operated flights. Results and analysis of test cases on trajectory optimization are presented using an in-house aircraft trajectory optimization framework created under the European Clean Sky Joint Technology Initiative, Systems for Green Operation Integrated Technology Demonstrator. The software tool comprises an optimizer core and relatively high fidelity models of the aircraft's flight path performance, air traffic control constraints, propulsion and other systems.

In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition.

Hybrid electric vehicles (HEV) are considered as the most cost effective solution, in the short term perspective, for the achievement of improved fuel economy (FE) and reduced emissions. This paper focuses on regenerative braking in a mild hybrid powertrain with infinitely variable transmission (IVT) and specifically on how its control strategy can be formulated and optimized. The study is conducted using a previously validated fully dynamic powertrain model. An initial investigation of the dynamic vehicle behaviour under braking conditions serves as the basis for the development of a control strategy for best braking performance and maximum energy recovery, the implementation of which requires a fully active and integrated brake control system. Limitations and constraints due to driveline configuration and driveability issues are considered and their effect evaluated. Simulation results show that fuel consumption reductions of 12% are achievable along a standard drive cycle.

As the essential of future driver assistance system, brake-by-wire system is capable of performing autonomous intervention to enhance vehicle safety significantly. Regenerative braking is the most effective technology of improving energy consumption of electrified vehicle. A novel brake-by-wire system scheme with integrated functions of active braking and regenerative braking, is proposed in this paper. Four pressure-difference-limit valves are added to conventional four-channel brake structure to fulfill more precise pressure modulation. Four independent isolating valves are adopted to cut off connections between brake pedal and wheel cylinders. Two stroke simulators are equipped to imitate conventional brake pedal feel. The operation principles of newly developed system are analyzed minutely according to different working modes. High fidelity models of subsystems are built in commercial software MATLAB and AMESim respectively.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.

With the increased interest in hybrid vehicle technology there is a need to investigate vast amounts of different hybrid vehicle topologies. Modelling and simulation plays an important role in this investigation process. In particular, modelling for controller development can quickly lead to model management and maintenance issues due to the variety of models required. The use of object oriented modelling languages can aid in plant model management by providing flexibility to different levels of users as well as reducing the number of separate plant models required for controller development. Two case studies are presented that illustrate some of the benefits gained from the object oriented modelling approach.

The aim of this paper is to develop a new concept of unconventional airship based on morphing a lenticular shape while preserving the volumetric dimension. Lenticular shape is known to have relatively poor aerodynamic characteristics. It is also well known to have poor static and dynamic stability after the certain critical speed. The new shape presented in this paper is obtained by extending one and reducing the other direction of the original lenticular shape. The volume is kept constant through the morphing process. To improve the airship performance, four steps of morphing, starting from the lenticular shape, were obtained and compared in terms of aerodynamic characteristics, including drag, lift and pitching moment, and stability characteristics for two different operational scenarios. The comparison of the stability was carried out based on necessary deflection angle of the part of tail surface.

The first cross-industry guidelines for the implementation of structural health monitoring for aerospace applications have been created as a SAE International Aerospace Recommended Practices document: SAE ARP 6461 ‘Guidelines for Implementation of Structural Health Monitoring on Fixed Wing Aircraft’ [1]. These guidelines have brought together manufacturers, operators / users, systems integrators, regulators, technology providers and researchers to produce information on the integration of SHM into aircraft maintenance procedures, generic requirements and advice on validation, verification and airworthiness. The take-up of SHM in the aerospace industry has been slow, in part due to the lack of accepted industry practices surrounding not just the technology itself (sensors and sensor systems) but also the associated issues arising from the introduction of new methods into aircraft maintenance.

The application of hybridization technology is now widely regarded as a significant step forward to reduce fuel consumption and hence CO₂ emissions for ground vehicles. Many programs and much research has been done on these technologies in the automotive market, however little work has been done in the very cost sensitive market sector of the small motorcycle. This paper introduces and discusses the application of a low-cost hybrid technology to small motorcycles and scooters, and reviews some of the initial trade-offs through the use of a new hybrid simulation model developed at Cranfield University. The study being presented assessed the existing Energy Storage Systems (ESS) in the market. This list was reduced, omitting options which posed a clear safety or cost risk, or solutions which would disproportionally increased the Gross Vehicle Weight (GVW). Also omitted were storage options which could not be production ready in the near term, 3 - 5 years.

Bus drivers are a group at risk of often suffering from musculoskeletal problems, such as low-back pain, while bus passengers on the last-row seats experience accelerations of high values. In this paper, the contribution of K-seat in decreasing the above concern is investigated with a detailed simulation study. The K-seat model, a seat with a suspension that functions according to the KDamper concept, which combines a negative stiffness element with a passive one, is benchmarked against the conventional passive seat (PS) in terms of comfort when applied to different bus users’ seats. More specifically, it is tested in the driver’s and two different passengers’ seats, one from the rear overhang and one from the middle part. For the benchmark shake, both are optimized by applying excitations that correspond to real intercity bus floor responses when it drives over a real road profile.

In today's aircraft the diagnostic and prognostic systems play a crucial part in aircraft safety while reducing the operating and maintenance costs. Aircraft are very complex in their design and require consistent monitoring of systems to establish the overall vehicle health status. Most diagnostic systems utilize advanced algorithms (e.g. Bayesian belief networks or neural networks) which usually operate at system or sub-system level. The sub-system reasoners collect the input from components and sensors to process the data and provide the diagnostic/detection results to the flight advisory unit. Several sources of information must be taken into account when assessing the vehicle health, to accurately identify the health state in real time. These sources of information are independent system-level diagnostics that do not exchange any information/data with the surrounding systems.

Co-production of aircraft is resulting in demands for higher standards of manufacturing quality to ensure that parts and sub-assemblies from different companies and countries are compatible and interchangeable. As a result the existing method of building aerostructure using large numbers of dedicated manufacturing jigs and assembly tools, is now seen as being commercially undesirable, and technologically flawed. This paper considers an alternative, potentially more cost-effective, approach that embraces digital design, manufacturing, and inspection techniques, and in which reference and tooling features are incorporated into the geometry of the component parts. Within the aerospace industry this technology is known as ‘Flyaway Tooling’.

The simulation of natural-like snow conditions in a controlled environment such as an Icing Wind Tunnel (IWT) is a key component for safe, efficient and cost-effective design and certification of future aircraft and rotorcraft. Current capabilities do not sufficiently match the properties of natural snow, especially in terms of size and morphology. Within the Horizon 2020 project ICE GENESIS, a new technology has been developed aiming to better recreate natural snowflakes. The focus of the newly developed system was the generation of falling snow in a temperature range of +1°C to -4°C. Ground measurements and flight test campaigns have been performed to better characterize these conditions and provide requirements for wind tunnel facilities. The calibration results of the new snow generation system as well as snow accretion data on a NACA0012 test article with a chord length of 0.377 m are presented.

Global aviation is growing exponentially and there is a great emphasis on trajectory optimization to reduce the overall environmental impact caused by aircraft. Many optimization techniques exist and are being studied for this purpose. The CLEAN SKY Joint Technology Initiative for aeronautics and Air transport, a European research activity run under the Seventh Framework program, is a collaborative initiative involving industry, research organizations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the overall research activities, “green” aircraft trajectories are addressed in the Systems for Green Operations (SGO) Integrated Technology Demonstrator. This paper studies the impact of large commercial aircraft trajectories optimized for different objectives applied to the on board systems.

This paper focuses on the investigation of the nature, process and effects of ice accretion on different feed pump strainers upstream of the aircraft feeding system. A suitable test rig was designed to circulate Jet A-1 containing water/ice contaminants at cold temperatures through the strainers. Following an extensive literature review, a number of screening tests were performed. These provided a strong base for an exhaustive study of fuel icing in the dynamic environment offered by the test rig. The effects of the rate of fuel cooling on the nature of ice were examined. As expected, it was observed that the yield of ice generated on the mesh screen increased with the water concentration in the fuel. It was also revealed that at higher cooling rates, a crust of snow formed on top of softer ice on the mesh screen.

UAS (Unmanned aircraft system), widely known to the general public as drones, are comprised of two major system elements: an Unmanned Aircraft (UA) and a Ground Control Station (GCS). UAS have a high mishap rate when compared to manned aircraft. This high mishap rate is one of several barriers to the acceptance of UAS for more widespread usage. Better awareness of the UA real time as well as long term health situation may allow timely condition based maintenance. Vehicle health and usage are two parts of the same solution to improve vehicle safety and lifecycle costs. These can be worked on through the use of two related aircraft management methods, these are: IVHM (Integrated Vehicle Health Management) which combines diagnosis and prognosis methods to help manage aircraft health and maintenance, and FOQA (Flight Operations Quality Assurance) systems which are mainly used to assist in pilot skill quality assurance.

Autonomous vehicles have been shown to increase safety for drivers, passengers, and pedestrians and can also be used to maximize traffic flow, thereby reducing emissions and congestion. At the same time, governments around the world are promoting the usage of battery electric vehicles (BEVs) to reduce and control the emissions of CO2. This has made the development of autonomous vehicles and electric vehicles a very active research area and has prompted a significant amount of government funding. This article presents the detailed design of a low-cost platform for the development of an autonomous electric vehicle. In particular, it focuses on the design of the electrical architecture and the control strategy, tailored around the usage of affordable sensors and actuators. The specifications of the components are extensively discussed in relation to the performance target.