Airbus (United States)

This paper addresses the challenge of probabilistic parameter estimation given measurement uncertainty in real-time. We provide a general formulation and apply this to pose estimation for an autonomous visual landing system. We present three probabilistic parameter estimators: a least-squares sampling approach, a linear approximation method, and a probabilistic programming estimator. To evaluate these estimators, we introduce novel closed-form expressions for measuring calibration and sharpness specifically for multivariate normal distributions. Our experimental study compares the three estimators under various noise conditions. We demonstrate that the linear approximation estimator can produce sharp and well-calibrated pose predictions significantly faster than the other methods but may yield overconfident predictions in certain scenarios. Additionally, we demonstrate that these estimators can be integrated with a Kalman filter for continuous pose estimation during a runway approach where we observe a 50% improvement in sharpness while maintaining marginal calibration. This work contributes to the integration of data-driven computer vision models into complex safety-critical aircraft systems and provides a foundation for developing rigorous certification guidelines for such systems.

Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes.

Electric vertical takeoff and landing aircraft have a unique duty cycle characterized by high discharge currents at the beginning and end of the mission (corresponding to takeoff and landing of the aircraft) and a moderate power requirement between them with no rest periods during the mission. Here, we generated a dataset of battery duty profiles for an electric vertical takeoff and landing aircraft using a cell typical for that application. The dataset features 22 cells, comprising a total of 21,392 charge and discharge cycles. 3 of the cells use the baseline cycle while each of the other cells vary either charge current, discharge power, discharge duration, ambient cooling conditions, or end of charge voltage. While it was designed to mimic the expected duty cycle of an electric aircraft, this dataset is relevant for training machine learning models on battery life, fitting physical or empirical models for battery performance and/or degradation, and countless other applications.

Early complications following periodontal and dental implant surgeries are typically attributed to technique or poor biological response, ignoring the possibility of the human element. Interestingly, significant experience is not correlated with increased success, whereas evidence supports the impact of clinical behavior on patient outcome. This is the result of errors, much like those scrutinized in other high-risk technical fields, such as aviation. What can be surprising is that those who make these errors are very well acquainted with best practices. Given this, how is it possible for the conscientious practitioner to fail to apply protocols that are nonetheless very well known? Recently, the concepts of human and organizational factors have been translated to medicine, though dentistry has been slow to recognize their potential benefit. This review lists specific human factor behaviors, such as use of checklists and crew resource management, which might improve postsurgical outcome.

A significant challenge in estimating operational feasibility of Urban Air Mobility (UAM) missions lies in understanding how choices in design impact the performance of a complex system-of-systems. This work examines the ability of the UAM ecosystem and the operations within it to meet a variety of demand profiles that may emerge in the coming years. We perform a set of simulation driven feasibility and scalability analyses based on UAM operational models with the goal of estimating capacity and throughput for a given set of parameters that represent an operational UAM ecosystem. UAM ecosystem design guidelines, vehicle constraints, and effective operational policies can be drawn from our analysis. Results show that, while critical for enabling UAM, the performance of the UAM ecosystem is robust to variations in ground infrastructure and fleet design decisions, while being sensitive to decisions for fleet and traffic management policies. We show that so long as the ecosystem design parameters for ground infrastructure and fleet design fall within a sensible range, the performance of the UAM ecosystem is affected by the policies used to manage the UAM traffic.

Airspace models have played an important role in the development and evaluation of aircraft collision avoidance systems for both manned and unmanned aircraft. As Urban Air Mobility (UAM) systems are being developed, we need new encounter models that are representative of their operational environment. Developing such models is challenging due to the lack of data on UAM behavior in the airspace. While previous encounter models for other aircraft types rely on large datasets to produce realistic trajectories, this paper presents an approach to encounter modeling that instead relies on expert knowledge. In particular, recent advances in preference-based learning are extended to tune an encounter model from expert preferences. The model takes the form of a stochastic policy for a Markov decision process (MDP) in which the reward function is learned from pairwise queries of a domain expert. We evaluate the performance of two querying methods that seek to maximize the information obtained from each query. Ultimately, we demonstrate a method for generating realistic encounter trajectories with only a few minutes of an expert's time.

The paper discusses recent results of flight tests performed with the Hensoldt (former Airbus DS Electronics and Border Security) LiDAR system in brownout. The SferiSense® LiDAR system was mounted on a Mi-2 test platform as part of the complete DVE system SFERION® to undergo the tests. To optimize brownout capabilities minor modifications were performed on the sensor firmware over the SferiSense® serial LiDAR system which is in operational use on the NH90 transport helicopter. Also dust echoes were filtered out by a real-time filtering algorithm. Numerous approaches into own ship generated dust (light to heavy) as well as fan generated dust clouds (chalk 2 scenarios) were performed. The paper discusses the results and shows under which conditions the LiDAR can still look through the dust cloud. Also the contribution of high resolution real-time 3D LiDAR data to the DVE system usage is discussed.

Compliant mechanisms, unlike rigid-body mechanisms, are devices that derive some or all of their mobility due to the deformation of their flexible members. The knowledge of existing rigid-body mechanism synthesis techniques is very useful in designing compliant mechanisms. In rigid-body mechanisms, a four-bar is treated as the basic mechanism that can transfer motion, force or energy. In this paper, a compliant single-strip continuum is introduced as the basic compliant mechanism that can transfer motion, force or energy. A classification of compliant mechanisms is presented herein. A methodology for compliant single-strip mechanism synthesis for energy, force or torque specifications is developed in this research as our second objective. The synthesis types, the governing equations, and the variables involved are enumerated.