Automatic Guidance Research Articles

Overlap Loss of Manually and Automatically Guided Mowers

Overlap loss in harvesting machinery has been observed as a necessary inefficiency for many years. Each time a non-row-crop machine makes a pass in the field it is favorable for the operator to overlap slightly into the previous pass, for instance where the crop has already been cut, as opposed to undercutting and leaving a strip of crop standing. In this study, overlap loss was explored through a controlled experiment as well as through on-farm survey. The experimental portion employed a factorial design to study the influence of two drivers and three cutting speeds (6.4, 9.7, and 12.9 km/h) of a self-propelled windrower. Driver experience and mowing speed were not found to have a significant effect on overlap. However, the interaction of inexperienced operators and higher mowing speeds increased overlap. The on-farm survey included three mower configurations: pull-type, self-propelled windrower, and mounted, as well as; cutting widths ranging from 4.01 to 9.60 m and a variety of field conditions on fifteen farms. Three of the mounted mowers and one self-propelled windrower utilized automatic guidance. Surveyed overlap ranged from 0.4 to 16.13% of machine cutting width. Average loss was 5% of cutting width. Automatic guidance has been purported to improve efficiency by eliminating time spent covering already mowed ground, reducing operator fatigue, and ensuring a uniform cutting pattern and swath density. The use of a GNSS-based guidance system to steer the mower was shown to reduce overlap loss from 5.03% to 2.34%.

A Robust Fuzzy Neural Control Approach for Vehicle Lateral Dynamics

This study addresses a robust control algorithm for automatic steering vehicles. The vehicle lateral dynamics is presented by a five degree-of-freedom model, which considers the nonlinear behaviors as tire force, wheel rotations, and forward velocity. Due to the model complexity, the analysis and design on this model is difficult to perform. To improve such problem, a robust control scheme which utilizes fuzzy neural network and backstepping control technique is investigated. The uniformly ultimately bounded stability conditions for ensuring performance are derived based on Lyapunov's method. Finally, the effectiveness of the proposed approach is verified via numeric example.

3315 左右制駆動力差とスクラブ半径により前輪操舵するmicro-EVの自動操舵と回避時間解析(OS5-2 運転支援,OS5 安全・安心・防災・環境負荷軽減,オーガナイズド・セッション(OS))

3315 左右制駆動力差とスクラブ半径により前輪操舵するmicro-EVの自動操舵と回避時間解析(OS5-2 運転支援,OS5 安全・安心・防災・環境負荷軽減,オーガナイズド・セッション(OS))

Experimental Verification of Active Front Steering Based on Driver-Friendly Reaction Torque Control

SUMMARY In recent years the demand for safer vehicles has increased. Active front steering (AFS) is an effective technology for stabilizing motion control after the detection of dangerous motion in a vehicle such as slip. However, if AFS intervenes excessively during the steering process, it becomes difficult to prevent interference between driver steering and AFS automatic steering. Therefore, at present vehicles are rarely equipped with AFS. This paper proposes a steering torque control method based on reaction torque estimation and variable damping control for use in a human-friendly AFS system and discusses how an experimental device was used to verify the effectiveness of this control method.

Neuro-Fuzzy Control with a Novel Training Method Based-on Sliding Mode Control Theory: Application to Tractor Dynamics

Accurate automatic guidance of agricultural vehicles is essential for gaining the ultimate benefit in agriculture. As an agricultural vehicle, tractors have more than one subsystem interacting each other, e.g. yaw dynamics, longitudinal dynamics, implement dynamics, etc. Instead of modeling the subsystem interaction prior to model-based control, we have developed a control algorithm which learns the interactions by using the measured feedback error. In this study, two PD controllers and two fuzzy neural networks are combined for controlling the yaw and traction dynamics. While the former ensures the stability of the related subsystem, the latter learns the system dynamics and becomes the leading controller. The interactions between both subsystems are not taken into account explicitly, but are considered to be disturbances in the control of each individual subsystem. A novel sliding mode control theory-based learning algorithm is used to train the fuzzy neural networks, and the convergence of the parameters is shown using a Lyapunov function.

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

AbstractThe conventional manual power line corridor inspection processes that are used by most energy utilities are labor intensive, time consuming, and expensive. Remote sensing technologies represent an attractive and cost‐effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing–based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed‐wing platforms for aerial data collection and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real‐time dynamic turning for flying between corridor segments; we call this approach PTAGS (Power line Tracking Automatic Guidance System). Improved object recognition is achieved by fusing information from multisensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches. © 2011 Wiley Periodicals, Inc.

Global-referenced navigation grids for off-road vehicles and environments

The presence of automation and information technology in agricultural environments seems no longer questionable; smart spraying, variable rate fertilizing, or automatic guidance are becoming usual management tools in modern farms. Yet, such techniques are still in their nascence and offer a lively hotbed for innovation. In particular, significant research efforts are being directed toward vehicle navigation and awareness in off-road environments. However, the majority of solutions being developed are based on occupancy grids referenced with odometry and dead-reckoning, or alternatively based on GPS waypoint following, but never based on both. Yet, navigation in off-road environments highly benefits from both approaches: perception data effectively condensed in regular grids, and global references for every cell of the grid. This research proposes a framework to build globally referenced navigation grids by combining three-dimensional stereo vision with satellite-based global positioning. The construction process entails the in-field recording of perceptual information plus the geodetic coordinates of the vehicle at every image acquisition position, in addition to other basic data as velocity, heading, or GPS quality indices. The creation of local grids occurs in real time right after the stereo images have been captured by the vehicle in the field, but the final assembly of universal grids takes place after finishing the acquisition phase. Vehicle-fixed individual grids are then superposed onto the global grid, transferring original perception data to universal cells expressed in Local Tangent Plane coordinates. Global referencing allows the discontinuous appendage of data to succeed in the completion and updating of navigation grids along the time over multiple mapping sessions. This methodology was validated in a commercial vineyard, where several universal grids of the crops were generated. Vine rows were correctly reconstructed, although some difficulties appeared around the headland turns as a consequence of unreliable heading estimations. Navigation information conveyed through globally referenced regular grids turned out to be a powerful tool for upcoming practical implementations within agricultural robotics.

Design of a Preview Controller for Vehicle Rollover Prevention

This paper presents a method for designing a preview controller for vehicle rollover prevention. It is assumed that a driver's steering input is previewable with a Global Positioning System (GPS) and an inertial measurement unit (IMU), or with an automatic steering system for collision avoidance. Based on a linear vehicle model, a linear optimal preview controller is designed. To avoid the full-state measurement of a linear quadratic regulator (LQR), linear quadratic static output feedback (LQ SOF) control is adopted. To compare with several types of controllers such as LQR or LQ SOF with respect to rollover prevention capabilities, Bode plot analysis based on a linear vehicle model is performed. To show the effectiveness of the proposed controller, simulations are performed on a vehicle simulation package CarSim.

Application of Fuzzy Neural Network in Ship Course Control

As the uncertainty of parameters and the random nature of disturbances that effect a ship’s course，one of the methods which can be used for designing a nonlinear ship course controller is the neural intelligent method. It is used here for designing a configuration of a nonlinear controller, which are then applied to ship course control. In the paper, we used fuzzy logic and neural network based on T-S model to design the controller. During the control design process, the structure of ship automatic steering and fuzzy neural network feedback controller were built. In order to enhance adaptive characteristics of the controller，the parameters of the network were optimized by a GA algorithm. The proposed course keeping method was tested in a simulation study involving a non-linear model of a ship. Simulation results show that the performance of the ship controller is valuable and easy to implement.

바이모달트램 안내제어기의 강인성 평가

This paper is concerned with the robustness evaluations of the guidance controller for a bimodal tram which is being developed by the Korea Railroad Research Institute (KRRI). The bimodal tram is an all-wheel steered multiple-articulated vehicle as a new kind of transportation vehicle. This vehicle has to be equipped with an automatic guidance system. In [1], such a controller has been recently proposed. However, since the performance is affected by weight change of the vehicle due to number of the passenger, model parameter uncertainties depending on the state of friction and the elasticity of the tire, and a typhoon, the controller designed must be examined with these conditions. As expected, because the vehicle dynamics is highly nonlinear, for the sake of investigating the robustness of the controller we compose two simulation ways based on the vehicle models which are implemented by the ADAMS and the MATLAB/LabVIEW toolboxes. Different uncertainties and a typhoon disturbance have been considered for the simulation conditions. Simulation results are shown.

Automatic guidance of underground mining vehicles using laser sensors

Autonomous guided vehicles are becoming extremely important in the mining industry. The underground environment is unique and complex in that the lighting conditions are poor, the roads are difficult to traverse, and no GPS can accurately receive signals in these areas. This paper analyzes the characteristics of commonly used positioning methods given that ordinary positioning and navigation methods do not work effectively underground. We outline the composition and principles in underground guidance of autonomous vehicles using the laser-based positioning system. This system is unique in that it draws on the premise of barcode theory, which allows for a feasible relative navigation system even without a GPS. Algorithms were developed to provide real-time control for an autonomous guided scraper. In the vehicle experiment, the scraper was able to autonomously move along the simulated laneway, and the trial average range longitudinal error was only 15 cm in a 40-m distance. Hence, this new type of positioning and navigation system can accurately and reliably guide vehicles.

Implementation of AVCS using Kalman Filter and PID Controller in Autonomous Self Guided Vehicle

Advanced Vehicle Control Systems (AVCS) is a key technology for Intelligent Transportation System (ITS) and Intelligent Vehicle Control System (IVCS). AVCS involves automatic steering, acceleration and braking control of fully autonomous vehicles. The unmanned control of the steering wheel is one of the most important challenges faced by the researchers. This paper proposes control architecture for automatic steering, acceleration and braking control of a self guided vehicle. Self guided vehicle is a line follower which tracks a black line on a white surface, through an array of infrared sensors. In line following, the readings that show deviation from the line are considered as lateral error and the proposed algorithm works towards minimizing the lateral error. Calculation of the lateral error and filtering the error values using Kalman filter is the first level of error calculation. Kalman filter protects the steering action from erroneous sensor values. PID control algorithm uses the filtered value and calculates the required steer to achieve the zero lateral error. The proposed adaptive speed control algorithm uses the speed boosting techniques for the longitudinal control of the vehicle. The experimental results show that the combination of Kalman filter with PID for lateral control reduces trajectory error to a minimum level and adaptive speed control algorithm for longitudinal control provides smooth accelerations over the entire track. Thus proposed algorithm gives better performance in both the lateral and longitudinal control.

Research on Path Tracking Control for Vision Based Intelligent Vehicle

This paper proposes an optimal control method to achieve the path tracking mission for the vision based intelligent vehicle. After the access of road trajectory, path tracking task is achieved by the intelligent vehicle automatic steering devices. The angle deviation and lateral deviation relative to the target path can be controlled in the smaller range by state feedback optimal control. A car model contained road information is established for the achievement of intelligent vehicle path tracking and automatic steering. Some values of the variables needed for the control system are obtained by sensors mounted on the car, and achieve the path tracking by the optimal state feedback controller. The algorithm proposed has been validated by simulations. It can make the lateral deviation within the expected range stability. And also the lateral acceleration meets the ride comfort requirements.

Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors

This paper evaluates the use of low-cost global positioning system (GPS) receivers in the autonomous guidance ofagricultural tractors. An autonomous guidance system was installed in a 6400 John Deere agricultural tractor. A lowcostGPS receiver was used as positioning sensor. Three different control laws were implemented in order to evaluate the autonomous guidance of the tractor with the low-cost receiver. The guidance was experimentally tested with the tracking of straight trajectories and with the step response. The total guidance error was obtained from the receiver accuracy and from the guidance error. For the evaluation of the receiver’s accuracy, positioning data from several lowcost receivers were recorded and analyzed. For the evaluation of the guidance error, tests were performed with each control law at three different speeds. The conclusions obtained were that relative accuracy of low-cost receivers decreases with the time; that for an interval lower than 15 min, the error usually remains below 1 m; that all the control laws have a similar behavior and it is conditioned by the control law adjustment; that automatic guidance with lowcost receivers is possible with speeds that went up to 9 km h–1; and finally, that the total error in the guidance is mainly determined by the receiver’s accuracy.

Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment

The sensitivity of various features that are characteristics of machine health may vary significantly under different working conditions. Thus, it is critical to devise a systematic feature selection (FS) approach that provides a useful and automatic guidance on choosing the most effective features for machine health assessment. This article proposes a locality preserving projections (LPP)-based FS approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP can find a good linear embedding that preserves local structure information. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The LPP-based FS approach is based on unsupervised learning technique, which does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of the proposed approach was evaluated experimentally on bearing test-beds. A novel machine health assessment indication, Gaussian mixture model-based Mahalanobis distance is proposed to provide a comprehensible indication for quantifying machine health state. The proposed approach has shown to provide the better performance with reduced feature inputs than using all original candidate features. The experimental results indicate its potential applications as an effective tool for machine health assessment.

A hybrid feature selection scheme and self-organizing map model for machine health assessment

The sensitivity of various features that are characteristics of machine health may vary considerably under different operation conditions. Thus it is critical to devise a systematic feature selection (FS) scheme that provides a useful and automatic guidance on choosing the most representative features for machine health assessment without human intervention. This paper presents a hybrid feature selection scheme named HFS based on the combination of Gaussian mixture models (GMM) and K-means. The proposed scheme is based on unsupervised learning technique, which does not need too much prior knowledge to improve its utility in real-world applications. The effectiveness of the scheme was evaluated experimentally on bearing test-beds, using degradation prediction and visualization approach where self-organizing map (SOM) model was employed. A novel health assessment indication, i.e., log likelihood probability (LLP) is proposed to provide a comprehensible indication to quantify machine health states. A Bayesian-inference-based failure probability (BIP) approach is proposed to calculate the failure risk probability of machines. This paper focuses on identifying the health states of the key machine component (i.e., bearing) under the assumption that the predictable abnormal patterns are not available. The proposed scheme has shown to provide the good machine health assessment performance with reduced feature inputs. The experimental results indicate its potential applications as an effective tool for machine health assessment.

Two-Wheel Self-Balancing of a Four-Wheeled Vehicle [Applications of Control

Cars and trucks are susceptible to accidents due to rollover. In the United States in 2005, 21.1% of a total of 54,718 deaths in vehicle crashes were caused by rollover [1]. Significant research has therefore been de voted to detecting and preventing rollover through active control. Numerous approaches attempt to detect or pre dict wheel liftoff using onboard sensing and a combina tion of automatic steering and braking to keep the wheels on the ground [2]-[4]. Rather than focusing on how to keep the wheels on the ground, it is also useful to under stand how to control a vehicle while two wheels are in the air. This understanding may enable the design of control laws that can safely return the vehicle to the ground after inadvertent tip-up.

Nomoto모델을 이용한 선박의 선형 모델 분석 및 퍼지제어기 설계

본 논문은 자동항로 추적(Track keeping control), 자동조타(Automatic steering), 자동 접이안(Automatic mooring control) 등으로 구성된 자동운항 시스템 중 자동조타장치의 성능 개선 알고리즘 개발에 대해 다루고 있다. 자동조타는 풍력 또는 조력 등의 영향으로부터 선박의 설정 항로와 실제 침로와의 차이를 계산하여 설정된 항로를 유지하며 항해하므로, 조타에 소요되는 선원의 지속적인 항해로 인한 운전 부담을 경감시키고 불필요한 타조작에 의한 항로 이탈을 줄임으로써 항해거리 단축과 연료비용을 절약할 수 있는 시스템이다. 선박의 모델링을 위하여 Nomoto 모델에 근거하여 전달함수를 구하고, 조종성능(Manoeuvirng) 편리성을 고려하여 타각 입력에 대한 선수각 응답으로 표시된 선박의 4자유도만을 고려한 선형 모델을 제안하고 선박 자동조타장치의 최대각과 타각율을 고려하여 Fuzzy제어기를 설계 하였고 PID제어기로 성능을 비교 분석하였다. This paper developed the algorithm for improving the performance the auto pilot in the autonomous vehicle system consisting of the Track keeping control, the Automatic steering, and the Automatic mooring control. The automatic steering is the control device that could save the voyage distance and cost of fuel by reducing the unnecessary burden of driving due to the continuous artificial navigation, and avoiding the route deviation. During the step of the ship autonomic navigation control, since the wind power or the tidal force could make the ship deviate from the fixed course, the automatic steering calculates the difference between actual sailing line and the set course to keep the ship sailing in the vicinity of intended course. first, we could get the transfer function for the modeling of ship according to the Nomoto model. Considering the maneuverability, we propose it as linear model with only 4 degree of freedoms to present the heading angle response to the input of rudder angle. In this paper, the model of ship is derived from the simplified Nomoto model. Since the proposed model considers the maximum angle and rudder rate of the ship auto pilot and also designs the Fuzzy controller based on existing PID controller, the performance of the steering machine is well improved.

Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance prediction without human intervention. This paper proposes a locality preserving projections (LPP)-based FE approach. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. The effectiveness of the proposed approach for bearing defect and severity classification is evaluated experimentally on bearing test-beds. Furthermore, a novel health assessment indication, Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) is developed to provide a comprehensible indication for quantifying bearing performance degradation. The proposed approach has shown to provide better performance than using regular features (e.g., root mean square (RMS)). The experimental results indicate potential applications of LPP-based FE and GMM as effective tools for bearing performance degradation assessment.