A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions

The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation

Impact of lesion delineation and intensity quantisation on the stability of texture features from lung nodules on ct: A reproducible study

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved

Visual Localization in the Presence of Appearance Changes Using the Partial Order Kernel

Visual localization across seasons and under varying weather and lighting conditions is a challenging task in robotics. In this paper, we present a new sequence-based approach to visual localization using the Partial Order Kernel (POKer), a convolution kernel for string comparison, that is able to handle appearance changes and is robust to speed variations. We use multiple sequence alignment to construct directed acyclic graph representations of the database image sequences, where sequences of images of the same place acquired at different times are represented as alternative paths in a graph. We then use the POKer to compute the pairwise similarities between these graphs and the query image sequences obtained in a subsequent traversal of the environment, and match the corresponding locations. We evaluated our approach on a dataset which features extreme appearance variations due to seasonal changes. The results demonstrate the effectiveness of our approach, where it achieves higher precision and recall than two state-of-the-art baseline method

A stochastically optimal feedforward and feedback technique for flight control systems of high performance aircrafts

This paper focuses on a detailed description of a control technique, which has been successfully used in several advanced flight control systems research projects over the past decade. The technique, called Stochastically Optimal Feedforward and Feedback Technique (SOFFT), directly descends from optimal control, and in particular from Explicit Model Following Control (EMFC). Unlike the most used model following techniques, in SOFFT the feedforward and feedback control laws are designed independently of one another. Moreover, this technique relies on different levels of plant models, specifically, a simple plant model is used for the synthesis of the feedback control law, and another plant model, together with a "command" model, are used in the synthesis of the feedforward control laws. It is important to notice that the controller in its final form is nonlinear in nature. This is because the matrices that compose the plant and command models are constantly updated as the aircraft moves throughout the flight envelope, and at least two Algebraic Riccati Equations (ARE) are solved in real time to compute the feedback and feedforward gains

A stochastically optimal feedforward and feedback technique for flight control systems of high performance aircrafts

This paper focuses on a detailed description of a control technique, which has been successfully used in several advanced flight control systems research projects over the past decade. The technique, called Stochastically Optimal Feedforward and Feedback Technique (SOFFT), directly descends from optimal control, and in particular from Explicit Model Following Control (EMFC). Unlike the most used model following techniques, in SOFFT the feedforward and feedback control laws are designed independently of one another. Moreover, this technique relies on different levels of plant models, specifically, a simple plant model is used for the synthesis of the feedback control law, and another plant model, together with a "command" model, are used in the synthesis of the feedforward control laws. It is important to notice that the controller in its final form is nonlinear in nature. This is because the matrices that compose the plant and command models are constantly updated as the aircraft moves throughout the flight envelope, and at least two Algebraic Riccati Equations (ARE) are solved in real time to compute the feedback and feedforward gains

Experimental interval models for the robust Fault Detection of Aircraft Air Data Sensors

In this paper data-based approaches for a robust Fault Detection (FD) of the Air Data Sensors (ADS) including airspeed angles of attack and sideslip are proposed. Experimental Interval Models (IMs) have been considered for coping with modeling uncertainty and for providing interval estimations of the ADS signals. Specifically, a nonlinear-in-the-parameter Neural Network model has been introduced to characterize the nominal nonlinear response in the different phase of the flight, while model uncertainty is captured by an additional additive contribution provided by a linear in the parameters IM. The FD is achieved by verifying whether the measured ADS signals fall within time-varying bounds predicted by the nonlinear + IM. The IM identification has been formalized as a Linear Matrix Inequality (LMI) problem using as cost function the mean amplitude of the prediction interval and, as optimization variables, the amplitudes of the uncertain parameters of the model. The model identification was based on multi flight experimental data of a P92 Tecnam aircraft. The proposed method is compared with conventional FD schemes with fixed thresholds. Extensive validation tests have been conducted by injecting artificially additive hard and incipient failures on the ADS. The FD scheme has shown to be remarkably robust in all phases of the flight in terms of low false alarm rates while maintaining desirable detectability to faults

Augmenting Flight Imagery from Aerial Refueling

© 2019, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. When collecting real-world imagery, objects in the scene may be occluded by other objects from the perspective of the camera. However, in some circumstances an occluding object is absent from the scene either for practical reasons or the situation renders it infeasible. Utilizing augmented reality techniques, those images can be altered to examine the affect of the object’s occlusion. This project details a novel method for augmenting real images with virtual objects in a virtual environment. Specifically, images from automated aerial refueling (AAR) test flights are augmented with a virtual refueling boom arm, which occludes the receiving aircraft. The occlusion effects of the boom are quantified in order to determine which pixels are not viable for stereo image processing to reduce noise and increase efficiency of estimating aircraft pose from stereo images

Combining Domain Adaptation and Spatial Consistency for Unseen Fruits Counting: A Quasi-Unsupervised Approach

Autonomous robotic platforms can be effectively used to perform automatic fruits yield estimation. To this aim, robots need data-driven models that process image streams and count, even approximately, the number of fruits in an orchard. However, training such models following a supervised paradigm is expensive and unpractical. Extending pre-trained models to perform yield estimation for a completely new type of fruit is even more challenging, but interesting since this situation is typical in practice. In this work, we combine a State-of-the-Art weakly-supervised fruit counting model with an unsupervised style transfer method for addressing the task above. In this sense, our proposed approach is quasi-unsupervised. In particular, we use a Cycle-Generative Adversarial Network (C-GAN) to perform unsupervised domain adaptation and train it alongside with a Presence-Absence Classifier (PAC) that discriminates images containing fruits or not. The PAC produces the weak-supervision signal for the counting network, that can then be used on the target orchard directly. Experiments on datasets collected in four different orchards show that the proposed approach is more accurate than the supervised baseline methods

Design and flight-testing of non-linear formation control laws

This paper presents the results of a research effort focused on the modeling, identification, control design, simulation, and flight-testing of YF-22 research aircraft models in closed-loop formation. These models were designed, manufactured, and instrumented at West Virginia University (WVU). The first phase of flight tests was performed with the goal of exciting all the aircraft dynamic modes. The recorded flight data were then used for a parameter identification study. The output of this Study was a mathematical model of the WVU YF-22 aircraft, which was then used for the design of the formation control laws. The design of the formation control laws is based on an inner/outer loop design with the objective of controlling the forward, lateral, and vertical distances between two aircraft in the formation. The design for the outer loop scheme was based on feedback linearization while a root locus-based approach was used for the design of the inner loop scheme. The paper presents experimental results validating the performance of the formation control laws using a 'virtual leader' configuration