Wheel Velocity Research Articles

ALBA: A comprehensive growth model to optimize algae-bacteria wastewater treatment in raceway ponds

This paper proposes a new model describing the algae-bacteria ecosystem evolution in an outdoor raceway for wastewater treatment. The ALBA model is based on mass balances of COD, C, N and P, but also H and O. It describes growth and interactions among algae, heterotrophic and nitrifying bacteria, while local climate drives light and temperature. Relevant chemical/physical processes are also included. The minimum-law was used as ground principle to describe the multi-limitation kinetics. The model was set-up and calibrated with an original data set recorded on a 56 m2 raceway located in the South of France, continuously treating synthetic wastewater. The main process variables were daily measured along 443 days of operations and dissolved O2 and pH were on-line recorded. A sub-dataset was used for calibration and the model was successfully validated, along the different seasons over a period of 414 days. The model proved to be effective in reproducing both the short term nycthemeral dynamics and the long-term seasonal ones. The analysis of different scenarios reveals the fate of nitrogen and the key role played by oxygen and CO2 in the interactions between the different players of the ecosystem. On average, the process turns out to be CO2 neutral, as compared to a standard activated sludge where approximately half of the influent carbon will end up in the atmosphere. The ALBA model revealed that a suboptimal regulation of the paddle wheel can bring to several detrimental impacts. At high velocity, the strong aeration will reduce the available oxygen provided by photo-oxygenation, while very low aeration can rapidly lead to oxygen inhibition of the photosynthetic process. On the other hand, during night, the paddle wheel is fundamental to ensure enough oxygen in the system to support algal-bacteria respiration. The model can be used to support advanced control strategies, including smart regulation of the paddle wheel velocity to more efficiently balance the mixing, aeration and degassing effects.

Path Tracking Simulation of a Wheeled Mobile Robot with Three Mecanum Wheels

This paper presents a study of wheeled mobile robot WMR path tracking by presenting the kinematic model of holonomic wheeled mobile robot with three mecanum controlled wheels. The advantage of mecanum wheels usage is to make the mobile robot movement in lateral and longitudinal directions smooth and to improve the tracking ability to travel in every direction under any orientation. This proposed type of WMR has three actuated input coordinates (angular velocities of the mecanum wheels) and three generalized output coordinates (robot linear translation along x, y axes and the robot orientation). The kinematic models of the WMR are based on a functional relation among configuration variables and their derivatives. It is a known fact that the WMR poses can be achieved in the configuration space but the path to reach these poses has complex step in the consideration. The mobility capability in the proposed design of WMR makes it suitable for different types of applications. The contribution in this work is the initiative to obtain the mathematical models of inverse and forward kinematics in order to analyze a new design of WMR with three mecanum wheels to simplify the task implementation of the robot by obtaining the suitable path to reach the mobile robot target. It has been taken in consideration that, the WMR motion is assumed as a pure rolling without any slipping. Simulation example results presented in this work show the proposed control algorithm applied on two different cases of path tracking (circular path, infinity path) and the effect of WMR wheels angular velocities on the robot body velocities and robot position errors. The results show a good performance in minimizing the positioning errors during the implementation of its task.

Acute hyperglycaemia can impair driving skill in young type 1 diabetes mellitus patients

AimDrivers with diabetes are at increased risk of being involved in road accidents. Therefore, this study aimed to evaluate the effects of acute hyperglycaemia (AH) compared with euglycaemia on driving ability in patients with type 1 diabetes mellitus (T1DM). MethodsEighteen drivers with T1DM were asked to navigate twice through nine hazardous scenarios, using a driving simulator, during euglycaemia and then again during AH (mean blood glucose: 138 ± 34 mg/dL and 321 ± 29 mg/dL, respectively) in a counterbalanced crossover study. Driving performance was continually monitored for driving speed, steering wheel angle, acceleration, and location and velocity of other vehicles and obstacles, with drivers wearing a mobile head-mounted eye-tracking system. ResultsThe main findings were that, during AH, participants were less likely to identify a hazard [probability of identification (POI): 0.5725 ± 0.5], glanced fewer times at the hazard (3.24 ± 5.9), maintained shorter headway (between-vehicle) distance (mean: 40.87 ± 20.15 m) and had an increased number of braking events per km driven (6.69 ± 5.20) compared with driving during euglycaemia (POI: 0.733 ± 0.4; number of glances: 3.69 ± 6.99; headway distance: 50.46 ± 26.2 m; number of braking events per km driven: 4.31 ± 3.87; P < 0.05 for all parameters). ConclusionThis study provides evidence that AH impairs driving performance in young T1DM patients by demonstrating the negative effects of AH on both hazard perception and speed management.

Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures

Drowsy driving is one of the main causes of road accidents. Accurate and reliable detection of drivers' drowsiness is significantly important to prevent drowsiness-related accidents. In the context of automated vehicle driving, it is important for intelligent systems to know the current state of the driver to prepare handover maneuvers. Previous studies are mostly based on manually extracted features from either driving performance or driver physiological data. This methodology of a priori defined features can lead to losing valuable information of input signals that are significant to classify drowsiness levels in individual drivers because generally, it is not known which features are suitable for drowsiness prediction before classification. By using deep neural networks, features can be extracted automatically from preprocessed data. This paper presents a new non-obtrusive drowsiness detection system based on deep neural networks using vehicle-based measures. The proposed method is based on a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). Five vehicle-based measures, including lateral deviation from road centerline, lateral acceleration, yaw rate, steering wheel angle, and steering wheel velocity, are exploited as network inputs. The level of drowsiness is classified into three different classes. Long-short term memory (LSTM) and gated recurrent unit (GRU) layers are used as RNN in the structure of the designed deep network. The performance of the proposed method is evaluated on experimental data that were collected from 44 sessions in a fixed-base driving simulator simulating monotonous night-time highway drives. Results show that the classification accuracy of the designed deep networks outperforms traditional classifiers like support vector machine and k-nearest neighbors. The highest accuracy of 96.0% has been achieved with a combination of CNN and LSTM (CNN-LSTM). Further research should include more signal sources, including unobtrusively taken physiological signals, and test the system in real-world conditions.

Revealing patterns in the wear of profile diamond wheels

The electro-erosive wheel dressing exerts a significant impact on the machining accuracy of an article and the efficiency of the profile grinding process. To effectively control machining accuracy, the most important is the error due to the wear of a diamond wheel. The wheel should be dressed when wear approaches to the border but does not go beyond the tolerance field. This would reduce the number of defects at machining and bring down the utilization of diamonds. In addition, this reduces the dressing time and, therefore, improves the efficiency of profile diamond grinding. The paper reports the methodology and results of an experimental study into the wear of profiled diamond wheels. The specific utilization of diamonds was examined on the grinding wheels of the direct profile. The amount of a wheel linear wear was measured by a contactless method applying a special device based on the use of eddy current sensors. This was followed by determining the integrated amount of the worn volume of a diamond layer; after that, the weight of the used diamonds was measured. The mass of the sanded material was determined by weighing it before and after machining. A mathematical method of planning and analysis of experiments was used to establish the functional dependence of the specific utilization of diamonds on the technological machining modes and the diamond layer parameters. The result of regression analysis is the derived functional dependence of diamond specific utilization on the following factors: the concentration of diamonds in a wheel, the grit of the tool, the grinding wheel velocity, the depth of grinding, and the speed of an article. To determine the patterns of wear of the shaped diamond wheel, we received an imprint of its profile on a control plate made from a solid alloy and measured the coordinates of the points of the working part of the profile relative to the non-utilized areas. Determining the difference of coordinates before and after the experiment has helped find the amount of radial wear of the grinding wheel at the appropriate profile point

Three-dimensional steering for an articulated mobile robot with prismatic joints with consideration of hardware limitations

ABSTRACT The paper presents a three-dimensional steering method for an articulated mobile robot that contains links, rotational joints, prismatic joints, and active wheels. The robot can change the angles of its links using the rotational joints and vary the lengths of its links using the prismatic joints. The target motion of this robot is represented by a continuous curve and the motions of the joints and the wheels are calculated by fitting the entire robot to this target curve. The forward velocity of the robot's head is adjusted to satisfy the hardware limitations of the robot; e.g. joint angle, joint velocity, and wheel velocity limitations. In addition, a terrain-following method is presented that considers the changes in the lengths of the links. An experimental articulated mobile robot was developed and experiments were carried out to demonstrate the effectiveness of the proposed method.

Simulation of curvilinear motion of automobile with the use of two-degree-of-freedom flat model

Development of active safety systems of automobiles is nowadays based not only on road tests, but also on computer simulation of vehicle's curvilinear motion. To properly perform simulation, all required model parameters have to be properly estimated. The less complicated model is, the less parameters it requires. So that, it makes no sense to apply too complicated models, if we are not able to estimate parameters with relatively low error. One of the most popular is two- degree-of-freedom flat model to describe curvilinear motion of automobile. It is widely used in design and improvement of active safety systems. The article discusses the application of simple two- degree-of-freedom flat model of automobile, which requires only several parameters. These parameters are: mass of a vehicle, location of center of gravity of a vehicle, yaw mass moment of inertia of a vehicle, side-slip characteristics. Furthermore, to be able to compare simulation and measurement results, it is necessary to know some input signals such as steering wheel angle and velocity of a vehicle, recorded during road tests. In this article signal of steering wheel angle was taken from Controller Area Network (CAN) bus. In case of model of a vehicle, the Authors decided to compare the results of simulation using two different side slip characteristics known as the dependence between lateral reaction force and side slip angle: linear characteristic (constant cornering stiffness) and the characteristic represented by Pacejka’s Magic Formula in steady-state version.

A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers.

Tires are essential components of vehicles and are able to transmit traction and braking forces to the contact patch, contribute to directional stability, and also help to absorb shocks. If these components can provide information related to the tire–road interaction, vehicle safety can be increased. This research is focused on developing the tire as an active sensor capable to provide its functional parameters. Therefore, in this work, we studied strain-based measurements on the contact patch to develop an algorithm to compute the wheel velocity at the contact point, the effective rolling radius and the contact length on dynamic situations. These parameters directly influence the dynamics of wheel behavior which nowadays is not clearly defined. Herein, hypotheses have been assumed based on previous studies to develop the algorithm. The results expose to view an experimental test regarding influence of the tire operational condition (slip angle, vertical load, and rolling velocity) onto the computed parameters. This information is used to feed a fuzzy logic system capable of estimating the effective radius and contact length. Furthermore, a verification process has been carried out using CarSim simulation software to get the inputs for the fuzzy logic system at complex maneuvers.

Rapidly solidified Sm-Co-Hf-B magnetic Nano-composites: Experimental and DFT studies

The effect of Hf and B additions on the phase stability, microstructure and magnetic properties of the metastable SmCo7 (1:7H) ribbons has been investigated with a combined approach of experimental measurements and first principle DFT (density functional theory) calculations. A series of (Sm0.12Co0.88)95Hf5−xBx (x = 0, 1, 2, 3, 4 and 5) alloys were arc-melted in a TIG (tungsten inert gas) arc melting furnace, followed by melt-spinning onto a copper roller at a wheel velocity of 40 m/s. Characterization based on X-ray diffraction indicates that the major phase is SmCo7 having meta-stable (TbCu7-type) structure. From the total energy calculations using DFT, the phase stability of (Sm0.12Co0.88)95Hf5−xBx ribbons have been confirmed. Moreover, Hf and B addition results in an effective grain refinement; average grain size being as low as ~ 80 nm. The reduction in grain size leads to significant changes (increase or decrease) in magnetic properties depending on the Hf/B ratio. The coercivity value (Hc) varies between 7 kOe and 12 kOe as x (at.% B) increases from 0 to 5 at.%. The experimental coercivity values have been compared with the computed anisotropy energies. The saturation magnetization (Ms) increases from ~ 54 emu/g to 77 emu/g with increasing B concentration (x).

Shimmy analysis of steering system with independent suspension based on coupled feedback model

Shimmy of steering system causes the snaking and deteriorates the handling stability and safety of vehicles, which has not been solved effectively for the ambiguity of shimmy characteristic. In order to uncover the mechanism of shimmy, a 5-DOF dynamic model of the steering system with independent suspension is established, and the coupled feedback model of the steering system is further obtained based on the dynamic model. Then the frequency characteristics of feedback are analysed, and the work and damping energy dissipation of each feedback link in a unit period are calculated. Based on the range of natural frequency and unit periodic energy of steering system, the range of wheel shimmy frequency and velocity interval is determined. The analysis results show that the shimmy frequency is the first natural frequency of steering system. The amplitude of wheel shimmy is calculated based on energy conservation, and the energy transfer of steering system is elaborated. The mechanism of shimmy was uncovered based on the energy transfer of coupled feedback system. The theoretical analysis was verified by numerical simulation. The results provide theoretical support for the mechanism research and active control of shimmy.

The effect of wheel and nozzle diameter ratio on the performance of a Turgo turbine with pico scale

A Turgo turbine with a pico scale (<5 kW) is one solution to supplying electrical energy in remote areas because of its low construction costs and ease of maintenance. Based on empirical equations, the ratio of the wheel (D) and nozzle diameter (d), or D∕d, of a Turgo turbine must be determined precisely because it affects wheel velocity, which has an impact on performance and/or efficiency. However, the optimum D∕d ratio has not yet been clearly determined. To find the optimum D∕d ratio for a Turgo turbine with a pico scale that can produce maximum electrical power, this study will examine five variations of the D∕d ratio: 11.6, 13.8, 14.7, 22 and 27.5. In order for this turbine to be implemented in remote areas, coconut shell is proposed as the material for its blades. The results of the analysis, which used a one-way analysis of variance (ANOVA) test with a confidence level of 92.5%, show that the D∕d ratio had a significant influence on performance. Based on the results, aD∕d ratio of 22 produces the highest efficiency – 34.97% with an electric power of 5.34 W (13.21 Vdc ± 0.1 Vdc and 396.4 mA ± 7.46 mA) – compared to the other ratios. Thus, the pico-scale Turgo turbine runner design is optimized​ at the D∕d ratio of 22. Furthermore, coconut shell blades can be implemented for a pico-scale Turgo turbine.

Nonparametric Bootstrap Technique to Improve Positional Accuracy in Mobile Robots With Differential Drive Mechanism

Wheeled mobile robots (WMRs) inevitably experience positional inaccuracy, which is caused by a number of factors including design imperfections, problems with component fabrication, sensor errors, and electromechanical malfunction. We have previously proposed an odometry-based technique that reduces positional inaccuracy in WMRs driven with standard wheels. This technique combines high sampling rates and short-term accuracy, and calculates necessary lateral and longitudinal corrections by using linear regression to model the relationship between positional inaccuracy and angular velocities of the robot's wheels. This technique can do so without the precision of measurement required by other techniques. In this paper, we discuss how a nonparametric bootstrap approach can be used to find both interval and point estimates of the modified angular velocities required to alleviate the positional inaccuracy of the WMR. First, the robot travels along a path recommended by the odometry-based error reduction technique. Then, the positional and angular errors of the robot at the stop point are measured. Next, these measurements are used to estimate angular velocities, providing the necessary confidence intervals (prediction). Results from these calculations could be incorporated into the robot program to modify the movement along a given path (validation). To show viability, the bootstrap technique was applied to a prototype mobile robot while the robot was programmed to move along an unseen trajectory. Results indicate that, for this typical unseen path, the bootstrap technique is capable of improving real-time positional systematic and non-systematic inaccuracy with acceptable levels of precision compared to the linear regression technique under the normality assumption. The bootstrap technique exhibited better efficacy than the linear regression, therefore, it may be a useful tool to conduct real-time calibration of differential drive WMRs.

Glocal Traction Control for In-wheel-motor Electric Vehicles - A Passivity Approach -

Although traction control of in-wheel-motor electric vehicles (IWM-EVs) has been studied for years, we still face the essential theoretical issue: How to assure the stability of the overall system while attaining both global and local control performances? This issue has been neglected by almost the works in the literature. The main reason is due to the nonlinearity and complexity in vehicle dynamics, especially the vehicle driven by multi-actuators. A new traction method is proposed in this paper in the glocal (global/local) framework to overcome this problem. The command to each local IWM includes two signals: (i) the local signal through anti-slip control in the lower-layer, and (ii) the global signal distributed from the driver or the upper-layer controller that managing the average wheel velocity. Stability of the whole system is guaranteed based on the fundamental passivity theory. The effectiveness of the proposed approach is verified by using Carsim/Matlab co-simulator.

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.

Improving the realism of a wheelchair simulator through tracking of upper body movements

Introduction Safe and efficient use of a manual wheelchair (MWC) requires appropriate training. New MWC users should learn how to maneuver in different environments (restricted spaces, uneven surfaces, etc.). Learning of appropriate balance skills is also crucial, to avoid falls when handling forward or side slopes. Moreover, long-term MWC use can lead to repetitive shoulder strain injuries, which can be avoided through adequate propulsion technique (increasing push amplitude, while decreasing cadence). We have developed a virtual reality MWC simulator for the training of MWC skills. The velocities of the rear wheels control a virtual MWC in a 3D scene presented on a computer screen in front of the participant, while force feedback simulates the effects of gravity and inertia. Our objective was to improve the realism of our existing wheelchair simulator, through the addition of upper body motion tracking that will provide feedback on trunk posture and propulsion movement. Material and methods Ten MWC users participated in the study. They propelled the virtual MWC while their trunk and arm movements were simultaneously recorded by three systems: – five inertial sensors placed on the trunk, arms and forearms; – a markerless, 3D depth camera system; – a high-precision motion capture system (gold standard). Upper body position as measured by the two low-cost approaches (inertial system and depth camera) was compared to the motion capture system. Results and conclusion Findings from this interdisciplinary study will optimize mobility of MWC users, through better MWC skills (balance and propulsion techniques), while possibly decreasing the incidence of falls and shoulder injuries.

Dynamic Motion Planning for Autonomous Wheeled Robot using Minimum Fuzzy Rule based Controller with Avoidance of Moving Obstacles

In this article, the Minimum Fuzzy Rule-Based (MFRB) sensor-actuator controller has designed for dynamic motion planning of a differential drive wheeled robot among the moving, non-moving obstacles and goal in two-dimensional environments. The ring of ultrasonic sensors and infrared sensors have been attached on the front side, left side, and right side of the wheeled robot, which detects the moving obstacles, as well as non-moving obstacles in any environment. This proposed MFRB sensor-actuator controller helps the wheeled robot to move safely in a different scenario. The onboard sensor interpretation data are fed as input to the MFRB controller, and the MFRB controller provides the Pulse Width Modulation (PWM) based wheel velocity commands to both the left and right motors of a wheeled robot. In the numerical simulation and experiment, we have taken one condition that the speed of wheels of the differential drive wheeled robot is at least more than or equal to the rate of the moving obstacles and the moving goal. The numerical simulations are performed through a MATLAB graphical user interface (GUI), and we have used the differential drive wheeled robot to conduct experiments. The presented numerical simulation and experimental results illustrate that the MFRB controller operated wheeled robot has successfully avoided the stationary and non- stationary obstacles in various scenarios.

A Novel Methodology for Dynamic Weigh in Motion System for Railway Vehicles With Traction

Loading conditions of railway vehicles both affect the vehicle and substructure directly. There are approaches to determine the load of a railway vehicle, the first one is to statically weigh the vehicle, the second approach is to place sensors along the track and dynamically weigh the vehicle at certain sections and the third one is to design special sensors that can be implemented on the vehicle. In this study, a model based indirect estimation methodology for normal load is proposed. This approach is based on interpreting angular velocities of wheels and translational velocity measurements of a vehicle to determine the normal load. A swarm intelligence based evolution of multiple models is proposed for estimation. In order to validate the approach, measurements taken from a tram wheel test stand with an independently rotating wheel are considered. The proposed approach is promising to be used as a dynamic weighing system and cost–efficient since only vehicle–based sensors are used. Furthermore, a continuous monitoring of the normal load is made possible with high accuracy since this methodology is neither limited to track–based sensors nor it requires special sensors and instrumented wheelsets.

Control of an articulated wheeled mobile robot in pipes

We propose a control method in which an articulated wheeled mobile robot moves inside straight, curved and branched pipes. This control method allows the articulated wheeled mobile robot to inspect a larger area. The articulated wheeled mobile robot comprises pitch and yaw joints is and propelled by active wheels attached to the robot. Via the proposed control method, the robot takes on two different shapes; one prevents the robot from slipping inside straight pipes and the other allows movement in a pipe that curves in any direction. The robot is controlled by a simplified model for the robot's joint angles. The joint angles of the robot are obtained by fitting to a continuous curve along the pipe path. In addition, the angular velocity of the robot's active wheels is determined by a simplified model. The effectiveness of the proposed the control method was demonstrated with a physical implementation of the robot, and the robot was able to move inside straight, curved and branched pipes.

INFLUENCE OF EXPLOITATION CONDITIONS ON ANTI-SKID PROPERTIES OF TYRES

Tyre-to-road adhesion plays an important role when taking into account transmission of forces between tyres and road surface. It consequently influences vehicle safety. Moreover, it plays a significant role for modelling vehicle motion, which is often applied in the development of automotive active safety systems and in traffic accidents reconstruction. Furthermore, tyre-to-road adhesion properties are dependent on many factors. One of the factors is the type of tyre – summer or winter. This is the reason why it is justified to study the anti-slip properties of summer and winter tyres. This paper shows the method of measuring tyre-to-road adhesion coefficient. It is based on a skid resistance tester SRT-4 that consists of a special dynamometer trailer, towing vehicle and test-measuring equipment. It was designed to be applied in civil/road engineering and further developed. As a result, the SRT-4 system automatically obtains adhesion characteristics, such as the graph of tyre-to-road adhesion coefficient as a function of wheel slip ratio and velocity characteristics of peak adhesion coefficient. Results of the study present the above mentioned characteristics for different types of tyres (summer, winter) in different exploitation conditions. Differences between presented characteristics caused by tyre type and conditions of exploitation are shown. For example, for winter tyres we noticed that the peak value of adhesion coefficient was attained for higher values of slip ratio as compared with summer tyres.

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-Type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this paper, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this paper, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.