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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.

In response to the ice crystal icing hazard identified twenty years ago, aviation industry, regulation authorities, and research centers joined forces into the HAIC-HIWC international collaboration launched in 2012. Two flight campaigns were conducted in the high ice water content areas of tropical mesoscale convective systems in order to characterize this environment conducive to ice crystal icing. Statistics on cloud microphysical properties, such as Ice Water Content (IWC) or Mass Median Diameter (MMD), derived from the dataset of in situ measurements are now being used to support icing certification rulemaking and anti-icing systems design (engine and air data probe) activities. This technical paper focuses on methodological aspects of the derivation of MMD. MMD are estimated from PSD and IWC using a multistep process in which the mass retrieval method is a critical step.

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.

Glaciated icing conditions potentially leading to in-service event are often encountered in the vicinity of deep convective clouds. Nowcasting of these conditions with space-borne observations would be of a great help for improving flight safety and air-traffic management but still remains challenging. In the framework of the HAIC (High Altitude Ice Crystals) project, methods to detect and track regions of high ice water content from space-based geostationary and low orbit mission are investigated. A first HAIC/HIWC field campaign has been carried out in Australia in January-March 2014 to sample meteorological conditions potentially leading to glaciated icing conditions. During the campaign, several nowcasting tools were successfully operated such as the Rapid Development Thunderstorm (RDT) product that detects the convective areas from infrared geostationary imagery.

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.

ChemCam is one of the 10 instrument suites on the Mars Science Laboratory, a martian rover being built by Jet Propulsion Laboratory, for the next NASA mission to Mars (MSL 2009). ChemCam is an instrument package consisting of two remote sensing instruments: a Laser-Induced Breakdown Spectrometer (LIBS) and a Remote Micro-Imager (RMI). LIBS provides elemental compositions of rocks and soils, while the RMI places the LIBS analyses in their geomorphologic context. Both instruments rely on an autofocus capability to precisely focus on the chosen target, located at distances from the rover comprised between 1 and 9 m for LIBS, and 2 m and infinity for RMI. ChemCam will help determine which samples, within the vicinity of the MSL rover, are of sufficient interest to use the contact and in-situ instruments for further characterization.

This study presents the results of the ICE GENESIS 2021 Swiss Jura Flight Campaign in a way that is readily usable for ice accretion modelling and aims at improving the description of snow particles for model inputs. 2D images from two OAP probes, namely 2D-S and PIP, have been used to extract 3D shape parameters in the oblate spheroid assumption, as there are the diameter of the sphere of equivalent volume as ellipsoid, sphericity, orthogonal sphericity, and an estimation of bulk density of individual ice crystals through a mass-geometry parametrization. Innovative shape recognition algorithm, based on Convolutional Neural Network, has been used to identify ice crystal shapes based on these images and produce shape-specific mass particle size distributions to describe cloud ice content quantitatively in details. 3D shape descriptors and bulk density have been extracted for all the data collected in cloud environments described in the regulation as icing conditions.

Measurements in snow conditions performed in the past were rarely initiated and best suited for pure and extremely detailed quantification of microphysical properties of a series of microphysical parameters, needed for accretion modelling. Within the European ICE GENESIS project, a considerable effort of natural snow measurements has been made during winter 2020/21. Instrumental means, both in-situ and remote sensing were deployed on the ATR-42 aircraft, as well as on the ground (ground station at ‘Les Eplatures’ airport in the Swiss Jura Mountains with ATR-42 overflights). Snow clouds and precipitation in the atmospheric column were sampled with the aircraft, whereas ground based and airborne radar systems allowed extending the observations of snow properties beyond the flight level chosen for the in situ measurements.

Testing real-time vehicular systems challenges the tester to design test cases for concurrent and sequential input events, emulating unexpected user and usage profiles. The vehicle response should be robust to unexpected user actions. Sequence Covering Arrays (SCA) offer an approach which can emulate such unexpected user actions by generating an optimized set of test vectors which cover all possible t-way sequences of events. The objective of this research was to find an efficient nonfunctional sequence testing (NFST) strategy for testing the robustness of real-time automotive embedded systems measured by their ability to recover (prove-out test) after applying sequences of user and usage patterns generated by combinatorial test algorithms, considered as “noisy” inputs. The method was validated with a case study of an automotive embedded system tested at Hardware-In-the-Loop (HIL) level. The random sequences were able to alter the system functionality observed at the prove-out test.

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.

Real-time analysis of benzene in automobile exhaust gas was performed using the Jet-REMPI (supersonic jet / resonance enhanced multi-photon ionization) method. Real-time benzene concentration of two diesel trucks and one gasoline vehicle driving in Japanese driving modes were observed under ppm level at 1 s intervals. As a result, it became obvious that there were many differences in their emission tendencies, because of their car types, driving conditions, and catalyst conditions. In two diesel vehicle, benzene emission tendencies were opposite. And, in a gasoline vehicle, emission pattern were different between hot and cold conditions due to the catalyst conditions.

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.

This paper gives an overview of the development of a number of loss models for the pushing metal V-belt CVT. These were validated using a range of experimental data collected from two test rigs. There are several contributions to the torque losses and new models have been developed that are based upon relative motion between belt components and pulley deflections. Belt slip models will be proposed based upon published theory, expanded to take account of new findings from this work. The paper introduces a number of proposals to improve the efficiency of the transmission based on redesign of the belt geometry and other techniques to reduce frictional losses between components. These proposed efficiency improvements have been modelled and substituted into a complete vehicle simulation to show improvements in vehicle fuel economy over a standard European drive cycle.

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.