Hybrid Kinematic-Dynamic Sideslip and Friction Estimation

Vehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error

Closed-loop Control of a Vibrant Duct Gravimetric Feeder

Abstract -In this work the control system for a gravimetric feeder with vibrant duct for polymer extrusion process has been designed. The plant considered in this paper is a blending machine that mixes up to six components: each component is measured by a dedicated vibrant duct. All components are mixed directly in the chambers of the extruder screw. The mass delivered by each meter is measured by a load cell. The control objectives are: accurate mass flow estimate on the basis of the weight; accurate mass flow regulation; minimum settling time; robustness of the controller parameters against the variation of material and structure of meter

Visual function improvement using photocromic and selective blue-violet light filtering spectacle lenses in patients affected by retinal diseases

Background To evaluate functional visual parameters using photocromic and selective blue-violet light filtering spectacle lenses in patients affected by central or peripheral scotoma due to retinal diseases. Sixty patients were enrolled in this study: 30 patients affected by central scotoma, group 1, and 30 affected by peripheral scotoma, group 2. Black on White Best Corrected Visual Acuity (BW-BCVA), White on Black Best Corrected Visual Acuity (WB-BCVA), Mars Contrast Sensitivity (CS) and a Glare Test (GT) were performed to all patients. Test results with blue-violet filter, a short-pass yellow filter and with no filters were compared. Results All scores from test results increased significantly with blue-violet filters for all patients. The mean BW-BCVA increased from 0.30 ± 0.20 to 0.36 ± 0.21 decimals in group 1 and from 0.44 ± 0.22 to 0.51 ± 0.23 decimals in group 2 (Mean ± SD, p < 0.0001 in both cases). The mean WB-BCVA increased from 0.31 ± 0.19 to 0.38 ± 0.23 decimals in group 1 and from 0.46 ± 0.20 to 0.56 ± 0.22 decimals in group 2 (Mean ± SD, p < 0.0001 in both cases). The letter count for the CS test increased from 26.7 ± 7.9 to 30.06 ± 7.8 in group 1 (Mean ± SD, p = 0.0005) and from 31.5 ± 7.6 to 33.72 ± 7.3 in group 2 (Mean ± SD, p = 0.031). GT was significantly reduced: the letter count increased from 20.93 ± 5.42 to 22.82 ± 4.93 in group 1 (Mean ± SD, p < 0.0001) and from 24.15 ± 5.5 to 25.97 ± 4.7 in group 2 (Mean ± SD, p < 0.0001). Higher scores were recorded with the Blue filter compared to Yellow filter in all tests (p < 0.05). No significant differences in any test results could be detected between the Yellow filter and the No filter condition. Conclusions The use of a combination of photocromic lens with a selective blue-violet light filter showed functional benefit in all evaluated patients

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

BACKGROUND: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. METHODOLOGY/PRINCIPAL FINDINGS: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. CONCLUSIONS: Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations

Measuring Urban Sidewalk Practicability: A Sidewalk Robot Feasibility Index

Autonomous parcel delivery is attracting a lot of interest. Terrestrial delivery drones travel at lower speeds, are smaller and lighter than passenger cars. These features make them an ideal and valuable first step and experimental sandbox toward fully autonomous vehicles. To be useful, however, small wheeled drones need to operate on parts of the roads that are reserved to pedestrians. This is a challenge by itself. Pedestrian areas are less structured than road and abide by looser rules. The best route for a delivery drone may not be the shortest path; other aspects need to be accounted for that make a route more or less practical for the specific features of the vehicle. This paper introduces a quantitative analysis of these specific issues. The paper proposes a quantitative index that asses a route practicability for a small terrestrial drone. It combines different aspects that account for sidewalk width, sidewalk surface condition, route length and the number of driveways and crosswalks present on the way. We provide the mathematical definition of the index, and use our wheeled drone prototype to show how it can be used to classify and chose the best routes among a selection. Although the index is designed for autonomous drones, given the specific dynamic features of the drone, it can also be employed as is to quantify the accessibility of different routes for disabled people

Data-driven Mass Estimation in Continuously Variable Transmission Agricultural Tractors

Many control vehicle systems are based on the knowledge of the current load. This trend has recently emerged also in heavy-duty and agricultural vehicles. Knowing the mass has in fact benefits both on safety and driving comfort. This paper proposes a mass estimation algorithm, based on already available sensors for an agricultural tractor equipped with a Continuously Variable Transmission. To this aim, the transmission model is derived, as the key ingredient of the mass estimate is the preliminary knowledge of the wheel torque. The latter, however, may be useful also for other control purposes, concerning, for example, longitudinal dynamics control. In the paper, the estimators proposed are applied on real world data

Combining tire-wear and braking control for aeronautical applications

In ground vehicles, tire consumption is in general mainly due to the mileage covered, and in fact the life span of tires, at least in common situations, is rather long. In the aeronautical context, and for aircraft in particular, instead, tire consumption plays a crucial role in determining the maintenance costs. This is due to the fact that, in aircraft braking, nearly all maneuvers activate the anti-skid controller, which remains in use for long time intervals. In ground vehicles, instead ABS systems are usually active for short time intervals which cover a part of the braking maneuvers only. Thus, tire consumption in the automotive context is usually studied under constant speed assumptions. In this work, we formulate a tire consumption models that encompasses explicitly the wheel acceleration/deceleration dynamics, and we show that tire wear can be directly related to the anti-skid controller parameters. Based on this, a sensitivity analysis of tire-consumption versus braking performance is carried out, showing that the braking control problem can be reformulated as a tire consumption regulation one

Extended target tracking for autonomous street crossing

Autonomous navigation on sidewalks and pedestrian areas is a complex problem, that requires the solution of different challenging tasks. One that is particularly hard to tackle is that of autonomous street crossing, which requires the robot to be aware of the position and speed of surrounding vehicles in order to decide whether is safe to cross. This work is dedicated to the development of an obstacle speed estimation algorithm to be applied to the context of autonomous navigation at crosswalks. In particular, a novel approach to the extended-target tracking problem is presented, which leverages a nested structure and a clustering algorithm that reduces the problem to a standard target tracking one. The effectiveness of the algorithm is demonstrated through testing on a prototype parcel-delivery robot operating in a real-world urban environment

A mixed sideslip yaw rate stability controller for over-actuated vehicles

Electronic stability control (ESC) has become a fundamental safety feature for passenger cars. Commonly employed ESCs are based on differential braking. Nevertheless, electric vehicles' growth, particularly those featuring an over-actuated configuration with individual wheel motors, allows for maintaining driveability without slowing down the vehicle. Standard control strategies are based on yaw rate tracking. The reference signal is model-based and needs precise knowledge of the friction coefficient. To increase the system robustness, more sophisticated approaches that include vehicle sideslip are introduced. Still, it is unclear how the two signals have to be weighted, and rarely proposed controllers have been experimentally validated. In this paper, we present a mixed sideslip and yaw rate stability controller. The mixed approach allows to address the control design as a single-input single-output problem simplifying the tuning process. Furthermore, we explain the rationale behind the choice of the weighting parameter. Eventually, the proposed ESC is validated following EU regulation in simulation and with an experimental vehicle on dry asphalt and snow. The results obtained in all the performed tests demonstrate that the proposed control strategy is robust and effective. The mixed approach is able to halve the sideslip in critical conditions with respect to a pure yaw rate approach