vision-based lateral control

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Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

The main aim of the project is to dynamic control design for automated driving of vision-based autonomous vehicles, with a particular focus on the coordinated steering and braking control in emergency obstacle avoidance. An autonomous vehicle uses a complex multi-input and multi-output structure, which possesses the features of parameter uncertainties and strong nonlinearities, and the fixed phenomena of lateral and longitudinal dynamics are clear in a combined cornering and braking movement. The effective coordinated control system for automated driving is wished-for to deal with these coupled and nonlinear features and eliminate the disturbances. First, a vision algorithm is constructed to detect the orientation path and offer the local location information between vehicles and reference path in real time. Then, a novel coordinated steering and braking control approach is proposed based on the nonlinear back stepping control theory and the adaptive fuzzy sliding-mode control method.
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Vision-based flight control in the hawkmoth Hyles lineata

Vision-based flight control in the hawkmoth Hyles lineata

Vision is a key sensory modality for flying insects, playing an important role in guidance, navigation and control. Here, we use a virtual-reality flight simulator to measure the optomotor responses of the hawkmoth Hyles lineata, and use a published linear-time invariant model of the flight dynamics to interpret the function of the measured responses in flight stabilization and control. We recorded the forces and moments produced during oscillation of the visual field in roll, pitch and yaw, varying the temporal frequency, amplitude or spatial frequency of the stimulus. The moths’ responses were strongly depen- dent upon contrast frequency, as expected if the optomotor system uses correlation-type motion detectors to sense self-motion. The flight dynamics model predicts that roll angle feedback is needed to stabilize the lateral dynamics, and that a combination of pitch angle and pitch rate feedback is most effective in stabilizing the longitudinal dynamics. The moths’ responses to roll and pitch stimuli coincided qualitatively with these functional pre- dictions. The moths produced coupled roll and yaw moments in response to yaw stimuli, which could help to reduce the energetic cost of correcting head- ing. Our results emphasize the close relationship between physics and physiology in the stabilization of insect flight.
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Vision-Based Control of a Full-Size Car by Lane Detection

Vision-Based Control of a Full-Size Car by Lane Detection

by a sensor in the steering wheel and output on the CAN bus at a high level of precision. Step inputs were initiated on the steering torque duty cycle signal ranging from 50% to 63% at 1% increments. Tests were performed at a large, flat, asphalt area with vehicle speeds ranging from 5 mph to 25 mph. Fig. 3.8 shows the results of the step input tests performed at 25 mph. It was observed that a general first order transfer function could be used to describe the relationship between steering torque duty cycle and steering wheel angle. However, at lower speeds and higher torque values, this observation is not valid. Fig. 3.9 shows the step response of the steering system at 15 mph. At the higher torque values, the steering wheel angles do not settle to a consistent steering wheel angle. It was also observed that the settling angles for a given steering torque duty cycle are not consistent for varying speeds. Therefore, the lateral model identification is speed dependent and would require a speed dependent limit on the steering torque duty cycle. Providing these characteristics, the system can still be modeled as first order transfer function for a given speed.
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Improving EV Lateral Dynamics Control Using Infinity Norm Approach with Closed-form Solution

Improving EV Lateral Dynamics Control Using Infinity Norm Approach with Closed-form Solution

The overall results are summarized in Tab. I and Tab. II. For both the 2 and the ∞ norm allocation, the velocity has been varied from 60km/h to 90km/h and the maximum steering angle from 2 ◦ to 4.5 ◦ . The steering angle command is shown in Fig. 5. It is translated by (32) into an yaw rate reference value for the controller. In Tab. I the RMS value of the yaw rate tracking error is shown. It is important to track the yaw rate very precisely in order to have a high performance lateral vehicle dynamics. Tab. II shows the maximal body slip angle occurring during the manoeuvre. A high body slip angle is undesired, dangerous, and should be avoided. For low velocities and small steering angles 2 norm and ∞ norm allocation behave equally well. For higher velocities and steering angles the ∞ norm is superior. Actuator saturation occurs later (shown with italic numbers) and the body slip
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Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

5.4. Crayfish Imaging. To further explore the performance of the artificial lateral line in a real-world environment and with real-world signals which are not exactly from dipole sources, we selected a tail-flicking crayfish as a hydrodynamic stimulus. The moving tail of the crayfish was brought near to the cylinder, close to the central sensor (Figure 6(a)). The signal recorded from the central sensor shows a pulsed pattern rather than the sinusoidal patterns generated by the dipole source (Figure 6(b)). However, still using the previous ideal template generated from the dipole source flow model, we applied the beamforming algorithm unaltered to the crayfish data and still achieved sharp localization results as shown in Figure 6(c). This confirms that the proposed method can robustly handle a real-world signal source even just using a simple dipole signal model in the beamforming algorithm.
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Hand Motion Recognition in an Intelligent Communication Module

Hand Motion Recognition in an Intelligent Communication Module

Abstract— our country India consists of very high population. Almost 9 millions of population in this world can be counted as a deaf or dumb people or both. We all know that the most valuable gift from the god given to human is the capability to watch, hear, spoke & to give response as per the situation arises. We all know communication is one of the most important medium through which one may carve up his/her feelings or express the information to others. The key points in communication are ability of listening and speak the word but so many from us are unlucky because they are not gifted by this ability from the god these are deaf & dumb and people. Now a days so many researches are going on to solve difficulties of these people of our society because they had to face very hard to communicate with normal person. It is too hard for mute(deaf & dumb) people to transmit their information to the normal people. As all normal people are not fully trained to understand different sign lingo, the communication between these two types of people becomes too complex. At the time emergency or other days whenever a mute (Deaf & Dumb) people are travelling passing a message (data) becomes too hard. Due to this disability one that has hearing and speaking disability doesn’t want to stand and face the race with normal person. Since the communication for the mute people is image (Visibility), not acoustic (Audio). For these mute people Hand motion plays a very part for communication. The data transmission between mute-deaf & normal people is always a very challenging job. Deaf people make use of sign language or gestures to make understand what he/she trying to say but it is impossible to understand by hearing people. The admittance to various communication based technologies plays an essential role
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Risk factors for amyotrophic lateral sclerosis

Risk factors for amyotrophic lateral sclerosis

Abstract: Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease. It is typically fatal within 2–5 years of symptom onset. The incidence of ALS is largely uniform across most parts of the world, but an increasing ALS incidence during the last decades has been suggested. Although recent genetic studies have substantially improved our understanding of the causes of ALS, especially familial ALS, an important role of non-genetic factors in ALS is recognized and needs further study. In this review, we briefly discuss several major genetic contributors to ALS identified to date, followed by a more focused discussion on the most commonly examined non-genetic risk factors for ALS. We first review factors related to lifestyle choices, including smoking, intake of antioxidants, physical fitness, body mass index, and physical exercise, followed by factors related to occupational and environmental exposures, including electromagnetic fields, metals, pesticides, β-methylamino-L-alanine, and viral infection. Potential links between ALS and other medical conditions, including head trauma, metabolic diseases, cancer, and inflammatory diseases, are also discussed. Finally, we outline several future directions aiming to more efficiently examine the role of non-genetic risk factors in ALS.
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Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

In the literature above, the longitudinal and lateral con- trol problems are considered separately. On one hand, to solve the problem of lateral motion control, a large num- ber of studies assumed that the velocity of the vehicle was a constant. On the other hand, most of the longitu- dinal control studies did not consider the lateral motion. However, a single lateral control or single longitudinal control cannot adapt to a complicated and fast-changing traffic  environment. Therefore, in order to improve the control effect in a wide range of vehicles, longitudinal and lateral control must be considered simultaneously. In many literatures, different control methods are proposed to solve the problems, for example, the longitudinal and lateral control in Ref. [17] was based on the sliding mode. The idea was to obtain the tire steering angle by calcu- lating the required tire force. However, a disadvantage is that this method is too complicated. The work in Ref. [18] described the design of driving control system, including both longitudinal and lateral controllers, for the Kuafu- II autonomous vehicle. Moreover, the controllers could achieve system robustness under diversified circum- stances. Ref. [19] dealt with a longitudinal–lateral control based on the nonlinear backstepping control theory and adaptive fuzzy sliding mode control. The control inputs in Refs. [18] and [19] were the brake, throttle, and steer- ing. In the references above, MPC, sliding mode control (SMC), gain-scheduling and feedback methods were usu- ally used to solve the control of longitudinal and lateral vehicle dynamics.
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Robot Control Based on Force and Vision Sensors

Robot Control Based on Force and Vision Sensors

cameras. To reduce the calculation demands some simplifications based on image analyses were introduced during the approach to the observed objects. In recent years robotic systems with the image sensor mounted on the end effector appeared. With this configuration the problems of occlusion can be avoided and a higher accuracy, because of a more focused area of interest, can be achieved [1]. The main problem of robotic systems based only on visual sensors are the optical characteristics of the observed objects required in order to achieve robust behavior of the 3D extraction algorithms. To establish and maintain a constant contact with the surface and simultaneously to track the desired trajectory, systems consisting of visual and force sensors were developed. Force sensors can be mounted in various ways. If the sensor is attached to the top of the tool the system is able to attain precise contour tracking [2]. Such a configuration disables the installation of a tool, therefore wrist-mounted force sensors are used in many cases.
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Development of Computer Vision based Windshield Wiper Control System for Automobiles

Development of Computer Vision based Windshield Wiper Control System for Automobiles

2. Capacitive sensor: A film of capacitive sensors are embedded within the laminated layer of the front windscreen. The sensor detects an intensity of rain based on a fluctuation of the capacitive signals and resistance due to a connection of electrodes of the sensor via rain-drops. The manufacturing process is a limitation for this system, as many car windshields are not laminated, plus electrodes of the sensors require electric connections, which is difficult and costly. Once the sensing electrodes are wet, they become less sensitive for rain sensing.
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Vision based interface system for hands free control of an intelligent wheelchair

Vision based interface system for hands free control of an intelligent wheelchair

a natural and friendly access method, however, the exist- ence of other noises in a real environment can lead to command recognition failure, resulting in safety prob- lems [13-15]. Accordingly, a lot of research has been focused on vision-based interfaces, where control is derived from recognizing the user's gestures by processing images or videos obtained via a camera. With such inter- faces, face or head movements are most widely used to convey the user's intentions. When a user wishes to move in a certain direction, it is a natural action to look in that direction, thus movement is initiated based on nodding the head, while turning is generated by the head direction. However, such systems have a major drawback, as they are unable to discriminate between intentional behavior and unintentional behavior. For example, it is natural for a user to look at an obstacle as it gets close, however, the system will turn and go towards that obstacle [16]. Our Proposal
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Microassembly of Complex and Solid 3D MEMS by 3D Vision-based Control

Microassembly of Complex and Solid 3D MEMS by 3D Vision-based Control

Fabrication of complex miniature sensor and actuator systems, hybrid Micro Electro Mechanical Systems (MEMS) or Micro Opto Electro Mechanical Systems (MOEMS) de- vices are gaining popularity. In the last decade, researches have been deliberately oriented towards the development of microrobotic cells to assist the human operator to handle or assemble such microparts, [3], [2], [17], [5]. In contrast to self-assembly [19], robotic microassembly is directed and deterministic and based on serial [22] or parallel [7] approaches. Recently, automation of microassembly tasks are one of ultimate goals. Meanwhile, the availability of high resolution cameras and powerful microprocessors have made possible for the vision systems to play a key role in the automatic microsystems assembly field. Therefore, vision sensor is essential to perform microhandling tasks, even in tele-operated mode and indispensable for automatic mode. Several vision techniques have been successfully implemented in the microdomain. It was shown that vision feedback control is an appropriate solution in automation of microhandling and microassembly tasks [18], [6], [11], [12]. Among tasks studied and described in the literature con- cerning automated assembly, we can cite the microassembly of type of peg-into-hole or 2D1/2 realization by stacking planar thin layers. In this paper, we focus on 3D complex
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Conical Tank Level control using Vision Position PID controller

Conical Tank Level control using Vision Position PID controller

ABSTRACT: The past few decades researchers much attention about the conical tank level control using various methods. Aim of the paper is to conical tank level control using vision position based PID controller, simulated and experimental results on the conical tank level control are presented. From the experimental results it is concluded that Vision Position PID controllers present the lowest energy consumption by the control signal.

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Hand Gesture Based Character Recognition using Neural Network

Hand Gesture Based Character Recognition using Neural Network

Abstract: Communication is the trading of thoughts, messages, or data, as by discourse, visuals, signs, composing, or conduct. Hard of hearing and unable to speak individuals impart among themselves utilizing gesture-based communications, however they think that its hard to open themselves to the outside world. This paper proposes a visual based strategy for Communication amongst hard of hearing and unable to speak individuals with the outside world utilizing computers. This strategy utilizes Indian communication via gestures hand motions given by the client as contribution through webcam and is changed over into an instant message. Not at all like the ordinary strategy for hand signal acknowledgment which makes utilization of gloves or markers or some other gadgets, this technique does not require any extra equipment and makes the client agreeable. Productivity is accomplished by utilizing a blend of various Algorithm together to extract features as opposed to depending on a solitary calculation.
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In process measurement of the surface quality for a novel finishing process for polymer additive manufacturing

In process measurement of the surface quality for a novel finishing process for polymer additive manufacturing

The machine learning module provides functionality to control the collaborative robot, to adjust camera settings and to train the software with a specific reference surface. The camera settings can be adjusted to find an optimal surface colour. An auto-exposure algorithm [18] and a white-balancing algorithm [19] are implemented to optimise the colour adjustment. This module allows the setting of the number of training data and the number of reduced dimensions from 2 to 54.

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Stability enhancement of railway vehicle 
		dynamics performance in lateral direction using fuzzy bogie based 
		skyhook control

Stability enhancement of railway vehicle dynamics performance in lateral direction using fuzzy bogie based skyhook control

However, it should be noted that the conventional skyhook algorithm treats all conditions without considering the moving direction between railway vehicle carbody and bogies. To overcome this problem, fuzzy logic control approach is adapted in these body-based skyhook and bogie-based skyhook control. Fuzzy logic is good to handle such a need because the desired damping constant can be determined by considering the moving direction between railway vehicle carbody and bogies. The output of the controller as determined by the fuzzy logic may exist between the high and low states damping. In fuzzy logic development, it is important to define certain parameters and conventions that will be used throughout the controller development. Referring to the Figure-2 and Figure-3, for all sign assignment, the movement of railway vehicle carbody and bogies are positive in clockwise direction.
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Persistence of Vision Control Using Arduino

Persistence of Vision Control Using Arduino

This paper mainly highlight on the POV (Persistence of Vision) technology. In current era in which energy is the main factor in designing all the applications, maximum and efficient use of the energy is very significant. To overcome the drawback of old processor we have decided to implement the same display a new and advanced microprocessor, the Arduino duemilanove. This platform brings with it newer coding and a different understanding of peripherals. Arduino interface boards provide us with a low-cost, easy-to use technology to create the project. We also aim to build the newer display to work with modern forms of interfaces. To accomplish this, we will be interfacing the display with an Android device. This project can be implemented with help of any Android Smartphone/tablet running Android 4.0+.
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Honeybee vision: analysis of orientation and colour in the lateral, dorsal and ventral fields of view

Honeybee vision: analysis of orientation and colour in the lateral, dorsal and ventral fields of view

The black-and-white gratings were created on a laser printer. The reflectance spectra of the white and black regions are given in Srinivasan and Lehrer (1984). The blue and yellow stimuli were constructed from commercially available coloured paper (Spectrum, Typofot AG, Wohlen, Switzerland). The reflectance spectra of these papers are given in Fig. 2 of Lehrer and Bischof (1995) as ‘Violet 1’ and ‘Yellow 2’ respectively. In both apparatus A and apparatus B, the honeybees had to enter one of the two tunnels at random (or based on expectations irrelevant to the experiment), since the two tunnels looked exactly the same from what used to be the decision chamber. Only after they had entered one of the tunnels were they offered a clue as to whether they had made the right choice. At that instant, they had to decide whether to accept the tunnel as the rewarded one and fly on to collect the reward, or to turn back when they had entered the wrong tunnel. Therefore, each test with these arrangements actually consisted of two separate tests, one in each tunnel, since a honeybee exploring one tunnel had no way of knowing what the other tunnel contained.
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Lateral line stimuli can override vision to determine sunfish strike trajectory

Lateral line stimuli can override vision to determine sunfish strike trajectory

Although all fishes have a mechanosensory lateral line system – a system of water flow detectors (neuromasts) in canals or free on the skin’s surface – particular behavioral functions are documented for only a few species. Work on lateral line use for feeding has focused on either cavefishes or night-active species (Montgomery, 1989). While surface- feeding fishes with well-developed eyes do use the lateral line to locate prey (Müller and Schwartz, 1982), it is generally assumed that diurnal/crepuscular fishes are visual predators. We show that a hydromechanical stimulus detected by the cephalic lateral line system in two sunfishes (Centrarchidae) can be the sole determinant of a strike trajectory. The response occurs without reinforcement and appears to be an unconditioned response. Green sunfish (Lepomis cyanellus Rafinesque) and largemouth bass (Micropterus salmoides Lacépède) are North American sunfishes that inhabit ponds, lakes and slow streams. Both species have diverse diets, including invertebrates and fishes (Carlander, 1977). These species and their congeners are active during the day; at night in field and laboratory they lie near or on the bottom and are lethargic (Neill and Magnuson, 1974; Helfman, 1981). Both species have cones and rods in the retina; the green sunfish visual pigments are characteristic of crepuscular mid-water fishes (Dearry and Barlow, 1987; Lythgoe and Partridge, 1989). Visual feeding of a congener of the green sunfish (L. macrochirus) has been studied previously (Li et al. 1985).
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Spontaneous Synchronization of Beating Cilia: An Experimental Proof Using Vision-Based Control

Spontaneous Synchronization of Beating Cilia: An Experimental Proof Using Vision-Based Control

As seen from the results, the synchronization is not perfect, as the differences in phase do not converge to the exact same values. Flap number i = 1 could not reach the phase relationship to the next as close as the others during the recording period of total number of 18 cycles. This could be related to the non-symmetric boundary effects for flap number i = 1 and i = 5. For both, the beating in direction away from the inner neighboring flaps is less influenced by viscous coupling than it is for the inner ones. As a result, the phase synchronization therefore may drift towards lock-on to either of the end flaps. A definite answer to the boundary effects can only be found by testing a chamber with a circular row of flaps, which eliminates these effects. However, variations in the phase shifts were also observed in the numerical simulations, even for imposed periodic boundary conditions [21]. Real-time control is, in general, sensitive to time constrains and system internal delays, which may lead to aliasing effects [32]. It takes 0.067 s from the detection of the terminal position to the action of the flap reversing the beat, given by the sampling frequency. At maximum tip speed, this delay equates to a possible variability of the amplitude of ∆ A/A i = 0.06. This uncertainty introduces a time variant parameter
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