Top PDF AGV Trajectory Control Based on Laser Sensor Navigation

AGV Trajectory Control Based on Laser Sensor Navigation

AGV Trajectory Control Based on Laser Sensor Navigation

transport goods and products in manufacturing fields where navigation can be done in a structured environment. In order to track the given trajectory, a tracking control based on Lyapunov stability theory is introduced. The use of the nonlinear Lyapunov technique provides robustness for load disturbance and sensor noise. To apply Lyapunov's theorem, the kinematic model of AGV is given. To recognize its position in indoor environment, in this paper, a laser sensor device NAV200 is used to detect the AGV position in real- time. For simulation and experiment, software and hardware are described. The AGV consists of 4 wheels with two passive wheels and two driving wheels. A controller is developed based on industrial computer. The effectiveness of the proposed controller is proved by simulation and experimental results .
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Application of A Star Algorithm in Robot for Catering Service Based on Laser Radar Navigation

Application of A Star Algorithm in Robot for Catering Service Based on Laser Radar Navigation

What used in this topic is linux operating system, which is control center of robot platform, integrates robot control, sensor data reception, immediate location and map, navigation based on map into elated functional module, operates for PC terminal of X86 architecture, it makes equipment transmit data by USB terminal through respective drive. Environment information collected by the robot is sent to the server and processed by the complex algorithm. The control commands are issued through the operating mechanism of the robot operating system, and the corresponding functions are completed [6]. The software framework of indoor autonomous mobile robot is shown in Figure 3, and the whole software framework is divided into three layers in figure: hardware layer, driving layer and application layer.
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Using Optical Mouse as a Position Feedback Sensor for AGV Navigation

Using Optical Mouse as a Position Feedback Sensor for AGV Navigation

mach ines[1]. Laser ranging technology usually supports a position and orientation computing rate of around 8 Hz [2]. Some formation control la ws are a lso proposed in diffe rent publications as easy alternatives for A GV control and navigation [3]. Over the past several decades AGV systems are widely accepted in manufacturing plants. One of the ma jor causes behind the popularity of such systems is that they require a little construction or floor alternation [4] to support transport carriers. In addit ion, A GV system is equipped with the proven navigation, align ment, safety and control system [5]. The develop ment of A GV control not only speaks about the industrial applicat ions but also shares the area of office and domestic service applications like partially autonomous wheelchair sand indoor and outdoor robot navigation. The key questions for the navigation of an automatic guided vehicle are the acquisition of current position of the vehicle, goal and the Way to accomplish the task [6]. Deadlocking is one of the ma jor challenges in AGV navigation and it can be avoided by using an efficient control and navigation system. Severa l researchers have carried out e xperiments regarding Dead lock-Detecting algorithm during the past several years [7, 8].
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Natural trajectory based FES induced swinging motion control

Natural trajectory based FES induced swinging motion control

Tuning a control loop refers to the adjustment of its control parameters to the optimum values for the desired control response. A major problem in the not so widespread use of fuzzy logic (FL) is the difficulty in designing of membership functions (MFs) to suit a given problem. A systematic procedure for choosing the vector of parameters that specify the MF is still not available. Given such plant model in the control loop, conventional stability analysis and tuning techniques are of no use. Consequently the tuning process of FL controller was suitably formulated as optimization procedure using genetic optimization techniques. Piecewise linear triangular MFs are used, because of their simplicity and efficiency with respect to computability. The FL controllers were optimized involving 73 decision variables or parameters. A breakdown of optimized parameters of the fuzzy system is as follows:
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Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

The environment and the robot are modeled using the Webots Pro simulator for mobile robot collision free navigation. The e-puck used has eight distance sensors which are infrared sensors, camera, three ground sensors, and GPS. The e-puck first senses the environment for possible collisions by using the distance sensors and the range finder camera readings. If there is no obstacle detected, the e-puck follows a black line drawn on a white surface. Snapshots of the simulation and real time experiment for one robot detecting and avoiding an obstacle while following the line are depicted in Figures 8 and 9 respectively. Both figures show the environment with one mobile robot moving forward until it detects an obstacle. After the detection of the obstacle, all readings are fed into the proposed fuzzy logic fusion model and, based on the defined fuzzy rules, the e-puck will turn accordingly by adjusting the left and right wheels velocities. After that, the e-puck will continue moving forward and follow the line.
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Trajectory tracking control of agricultural vehicles based on disturbance test

Trajectory tracking control of agricultural vehicles based on disturbance test

(1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, Shandong, China; 2. Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Taian 271018, Shandong, China; 3. College of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China) Abstract: To improve the trajectory tracking robust stability of agricultural vehicles, a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented. Through the proposed method, the path tracking problem can be divided into two problems with speed and steering angle constraints: the trajectory planning problem, and the trajectory tracking optimization problem. Firstly, the nonlinear kinematics model of the agricultural vehicle was discretized, then the derived model was inferred and regarded as the prediction function plant for the designed controller. Second, the objective function characterizing the tracking performance was put forward based on system variables and control inputs. Therefore, the objective function optimization problem, based on the proposed prediction equation plant, can be regarded as the nonlinear constrained optimization problem. What’s more, to enhance the robust stability of the system, a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control. To validate the theoretical analysis before, the Matlab simulation was performed to investigate the path tracking performance. The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability. Meanwhile, the corresponding experiments were conducted. When the test vehicle tracked the reference track with a speed of 3 m/s, the maximum lateral deviation was 13.36 cm, and the maximum longitudinal deviation was 34.61 cm. When the added horizontal deviation disturbance Yr was less than 1.5 m, the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive. Finally, to better highlight the controller proposed in this paper, a comparison experiment with a linear model predictive controller was performed. Compared to the conventional linear model predictive controller, the horizontal off-track distance reduced by 36.8% and the longitudinal deviation reduced by 32.98% when performing circular path tracking at a speed of 3 m/s.
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Optical fiber Hemoglobin concentration sensor
based laser polarization

Optical fiber Hemoglobin concentration sensor based laser polarization

Optical fiber sensor for measuring hemoglobin concentration (Hb) of human peripheral blood depending on polarization is designed and implemented during this work. Its present a simple and accurate method to measure Hb concentration. Three lasers have been used in this system. These lasers are He-Ne laser with (632nm) wavelength, (5 mw) power, laser diode with (532nm) wavelength, (20 mw) power and another laser diode with(430nm) wavelength, (20 mw) power. biosensors use absorbance measurements to determine any changes in the concentration of analytes that absorb a given wavelength of light. The system works by transmitting light through an optical fiber to the sample; the amount of light absorbed by the analyte is detected through the same fiber or a second fiber.
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Adapted Markovian Model to Control Reliability Assessment in Multiple AGV

Adapted Markovian Model to Control Reliability Assessment in Multiple AGV

In the Generalized Renewal Process (GRP) relia- bility analysis for repairable systems, the Monte Carlo (MC) simulation method, instead of numerical method, is often used to estimate the model parameters because of the complexity and the diculty of developing a mathematically tractable probabilistic model. Wang and Yang [25] proposed a nonlinear programming approach to estimate the restoration factor for the Kijima type GRP model I, as well as the model II, based on the conditional Weibull distribution for repairable systems, using negative log-likelihood as an objective function, and adding inequality constraints to model parameters. The method minimized the negative log-likelihood directly, and avoided solving the complex system of equations. Three real and dierent types of eld failure data sets with time truncation for NC (Numerical Control) machine tools were analyzed by the proposed numerical method. The sampling formulas of failure times for the GRP models I and II were derived, and the eectiveness of the proposed method was validated with the MC (Monte Carlo) simulation method.
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Portable Laser Photoacoustic Methane Sensor Based on FPGA

Portable Laser Photoacoustic Methane Sensor Based on FPGA

A schematic of the portable PA methane sensor is shown in Figure 1. A tunable DFB, fiber-coupled diode laser (NLK1U5EAAA, Central Wavelength 1653.72 ± 0.05nm , 10 mW, FC/PC pigtailed, NEL, Japan) is used as the excitation light source. The laser beam is collimated using a fiber optical collimator (Operating Wavelength 1650nm, Primanex, China) for direct installations in the resonant PA cell. The DFB diode laser is operated in wavelength modulation mode by the laser controller. Modulation of the laser current is performed by applying a sinusoidal dither to the direct current ramp of the DFB diode laser at half of the PA cell’s resonance frequency. The PA signals are detected by a Microphone (EK3033, 22mV/Pa) which is placed in the middle of the PA cell resonator. The amplified PA signals by a high input impedance preamplifier (AD8221,ANALOG DEVICES, USA) are measured using the lock-in amplifier based on FPGA with a time constant 1s. Gas concentration is displayed in digital display after the data acquisition and processing. The photograph of the portable PA sensor is shown in Figure 2 (220×200×80).
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Association between mild cognitive impairment and trajectory-based spatial parameters during timed up and go test using a laser range sensor

Association between mild cognitive impairment and trajectory-based spatial parameters during timed up and go test using a laser range sensor

aspects of spatial gait that clinicians would not be able to evaluate just using a stopwatch. In addition, in the LRS system, the LRS position is able to be calibrated using two calibration poles in the TUG field coordinate system easily. Therefore, the system is able to directory measures trajectory-based spatial parameters in the TUG field: distance from the marker, distance from the x -axis, and area of region surrounded by the trajectory of the center of gravity and the x -axis. To measure these parameters using IMUs (Inertial Measurement Units) [11, 14], it is also necessary to calculate the initial posture of the IMUs in the TUG field coordinate system every time before the test. However, it is difficult to calibrate with high precision when the IMUs attached to the legs of the participants, and it takes time and labor to measure TUG tests several times per participant. On the other hand, compared to the IMU system, the LRS system cannot measure the leg angular parameters dir- ectory. In addition, when a long-distance walk test which is longer than TUG test is measured, the stationary LRS measurement system is impossible to measure the walk outside of the sensor range. However, it is necessary to assess many participants in a short time in actual com- munity health centers. The LRS system is a non-contact measurement system and realizes smooth measurement at the scene. In addition, the system can be applied to quantitatively assess abnormal gait in patients with neurological disorders such as Parkinson’s disease and stroke. Further studies are required to understand the neural mechanisms underlying these associations and to evaluate the feasibility of trajectory-based mobility par- ameter assessments in the context of other disorders.
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BCI-based spatial navigation control: a comparison study

BCI-based spatial navigation control: a comparison study

Row and column scores given by the respective algorithms were summed to compute a unique score for each cell in each pair of trials (1 trial in which rows were flashed and 1 trial in which columns were flashed). For example, the score for 'B,' which was located in the first column and the second row, was the sum of the score for the first column and the score for the second row. The scores computed for each letter were summed across trials to determine which cell was identified as the cell selected by the subject. Each test could yield 1 correct response or 1 of 35 possible errors. The test was considered a 'hit' if the algorithm yielded the largest total score, summed across trials, for the letter on which the subject was focusing. For example, if the subject was attending to the letter 'B' and 6 trials were being considered in the analysis, a correct response would be achieved if the total of the 6 'B' scores - the scores for the rows and columns containing 'B' - was greater than the total of the 6 scores for any other cell in the matrix. SWLDA and peak picking proved to be the most efficient algorithms, which in the context and purpose of the experience means the fastest algorithms to reach both 80% and 95% accuracy in 3 out of 4 cases. Later in 2004, Bayliss et al [64], developed an ERP-based BCI set in a Virtual Reality (VR) environment, using Peak picking and Correlation, as chosen algorithms for feature extraction and classification. Seven electrode sites were arranged on the heads of nine subjects with a linked mastoid reference. Sites 𝐹𝑧, 𝐶𝑧, 𝑃𝑧, 𝑃3, 𝑃4, as well as an upper and lower vertical EOG channels were used. For online recognition and analysis, EOG artifacts were regressed out of the signals of interest using an algorithm by Semlitsch [65]. The EEG signals were amplified using Grass amplifiers with an analog bandwidth of 0.1– 100Hz. Electrode impedances were 2– 10kΩ for all subjects. An epoch size from −100ms (prior to stimulus onset) to 1500ms was specified for a total epoch size of 1600ms. The data was recorded continuously and saved to a file.
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Trajectory Generation and Control for Quadrotors

Trajectory Generation and Control for Quadrotors

Aggressive maneuvers with aerial robots is an area of active research. Exciting re- sults have been demonstrated for perching with fixed-wing aerial vehicles [25, 27] as well perching on inclined surfaces with a small helicopter [18]. A number of groups have demonstrated aggressive aerial maneuvers with small-scale rotorcraft [9, 33, 54]. In this area considerable effort is focused on strategies for generating sequences of controllers that stabilize the robot to a desired state. In [32, 33], Gillula et al. present an optimization- based control design methodology that generates a sequence of stabilizing controllers that drive a robot to a hover state after entering a flipping maneuver. The authors are able to provide guarantees of recovery from a flipping maneuver based on the robot model and present experimental results to validate their approach. Tedrake proposes an optimization- based design methodology with similar guarantees using a guided sparse sampling of the state space and creating sequences of stabilizing controllers that drive the system to a desired state through a sequence of sampled states [75].
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Design An Automated Guided Vehicle (AGV) Point To Point Motion Control

Design An Automated Guided Vehicle (AGV) Point To Point Motion Control

This project is about to create and develop the Automated Guided Vehicle (AGV) which is by considering three major parts that is design and build a prototype of an AGV, develop the control system for AGV and improve the motion of the AGV by using a suitable controller and improving the design that may occurring problem to the motion of AGV. This project involves of parts from sketching, drawing, measuring each dimension to the control system part which involves computing wiring system and software application to ensure the AGV can run perfectly. This project is proposed to design an Automated Guided Vehicle point-to- point motion control. An AGV is fabricated by using DC motor and a basic controller is designed to control the motion. The controller is implemented for a line following robot to analyze the controller robustness.
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Penetration depth control using an optical spectroscopic
sensor in ND:Yag laser welding

Penetration depth control using an optical spectroscopic sensor in ND:Yag laser welding

In the research conducted for this thesis, radiation from the Laser material interaction zone is measured in order to produce a spectral signal which monitors a predefined weld defect i.e. lack of penetration. Such sensor data can be used in several ways. Firstly, by acquiring a stable spectral signal from the plume radiation, the weld process is controlled in real-time to prevent the weld defect. In this thesis a spectrometer is used as a measuring device and the Laser power is used as a control actuator. Konuk et al. (2009) have used the spectrometer within the CLET project (Closed Loop control of laser welding through Electron Temperature) to find a reliable method for detection and avoidance of weld defects in Laser welding. In this thesis a new signal has been defined to detect and avoid lack of penetration. A second application of the sensor is based on the research by Oiwa et al. (2011). They used a 1070 nm fiber Laser for welding experiments and have shown that blowing the plume away with air has proven to increase the penetration depth, thus increasing the welding efficiency. The purpose of this work is to combine and go beyond earlier work done by Oiwa and Konuk in such a manner that the weld process is first monitored and then controlled in real-time while the weld efficiency is increased.
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Optimized fuzzy control for natural trajectory based fes swinging motion

Optimized fuzzy control for natural trajectory based fes swinging motion

Many control strategies have been developed to provide enhanced reproducibility of muscle responses, including fixed-parameter feedback control [4-51, model reference adaptive control [6[r]

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Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

On the other hand, fuzzy logic control (FLC) has long been known for its ability to handle a complex nonlinear system without developing a mathematical model of the system. It is being used successfully in an increasing number of application areas in the control community. FLCs are rule-based systems that use fuzzy Abstract: The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI). A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment.
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Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

A track vehicle has good passing ability, low grounding pressure, and high adhesion to the ground. It can work in harsh environments, such as soft land and wetlands. Especially in dangerous or narrow situations, a crawler travel mechanism is often used, such as mine-sweeping robots, fire-fighting robots, and deep-sea mining robots [1] and [2]. Automatic navigation or trajectory-tracking technology for track vehicles is necessary for these vehicles. [3] and [4] The automatic movement of the vehicle mainly refers to automatic control the vehicle to reach the designated target, according to the predetermined reference trajectory and the state of the vehicle and the environmental information [5]. Furthermore, trajectory tracking requires the controlled object to reach the specified reference point within a given time [6]. Vehicle mathematical model analysis plays a key role in solving the trajectory tracking control problem of unmanned track vehicles. However, track vehicles are complex nonlinear systems, and it is difficult to establish accurate mathematical models. At the same time, high-precision control problems need to consider many uncertain factors, which can lead to a high complexity of control problems.
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A Component-Based Navigation-Guidance-Control Architecture for Mobile Robots

A Component-Based Navigation-Guidance-Control Architecture for Mobile Robots

The software architecture is based on the main NGC pattern which was completed by adapters to convert some of the non-generic data types, a joystick and a switch to keep manual control on the system if desired. A planner is also plugged onto the Navigation component to provide potential parallel planning with different algorithms (Fig. 2).

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Models for Pedestrian Trajectory Prediction and Navigation in Dynamic Environments

Models for Pedestrian Trajectory Prediction and Navigation in Dynamic Environments

In ROS, there are global planners and local planners. Global planners use the costmaps and plan a route to the destination that minimizes length of the path while also avoiding dangerous (darker) areas on the map. The basic global planner uses the A* search algorithm. A* is an efficient search method that uses heuristics to focus planning on regions of the map that are more likely to yield optimal routes to the destination. The local planners use the route defined by the global planner and outputs instructions for the robot to ensure that the robot uses its actuators to follow the global plan as closely as possible. A key task for building successful robots is to write a system that correctly balances long-range and short-range planning. In the simplest implementation, neither the local planners nor the global planners consider the movement of other agents. Of course, sensors can detect pedestrians or moving vehicles, but the robot treats these the same as static obstacles. This is a major lim- itation because it means that robots using these navigation tools cannot effectively move in dynamic environments without colliding or being overly cautious and failing to make forward progress. A navigation system that will succeed in dynamic envi- ronments must incorporate knowledge of other agent’s trajectories at both the local and global levels.
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