Automatic Guidance Research Articles

Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

A Navigation and Augmented Reality System for Visually Impaired People.

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

Improving the manual harvesting operation efficiency by coordinating a fleet of N-trailer vehicles

In the last few years, automatic guidance of agricultural vehicles has received increased interest to improve field efficiency, while releasing human workers from monotonous operations. In this context, this work proposes a short or medium-term alternative to complete automation of manual harvesting processes by introducing a fleet of robotic N-trailer vehicles to support the crop transportation task. With the aim of coordinate several vehicles which share the workspace with human pickers, this work presents a decentralized cooperative navigation strategy that divides the field into harvesting areas, allocates an active N-trailer vehicle per area, determines the number of trailers to use according to the payload capacity and maneuverability constraints, and coordinates the departure time of backup N-trailers in order to reduce non-productive times. The proposed strategy includes a harvesting sequence generation stage based on centralized global information, and a dynamic route planning stage based on both global and local information. In order to make the navigation strategy robust against the variability on the harvesting rate and the uncertainties about the field productivity, the global information is continuously updated based on local information from the vehicles. A simulator was built in order to evaluate the performance of the proposed strategy in realistic scenarios having different field productivity and machinery availability. Thus, by using a large fleet of N-trailers and properly coordinated them to share the workspace, the results showed an efficiency improvement of up to 82% with respect to the basic case where two vehicles (one active vehicle and one backup vehicle) were used to cover the whole field, as reported in the literature.

Intact Goal-Driven Attentional Capture in Autistic Adults.

Background:Autistic individuals have been found to show increased distractibility by salient irrelevant information, yet reduced distractibility by information of personal motivational salience. Here we tested whether these prior discrepancies reflect differences in the automatic guidance of attention by top-down goals.Methods:Autistic (self-reported diagnoses, confirmed with scores on the Social Responsiveness Scale) and non-autistic adults, without intellectual disability (IQ > 80 on Wechsler Abbreviated Scale of Intelligence), searched for a color-defined target object (e.g., red) among irrelevant color objects. Spatially uninformative cues, matching either the target color or a nontarget/irrelevant color, were presented prior to each display.Results:Replicating previous work, only target color cues reliably captured attention, delaying responses when invalidly versus validly predicting target location. Crucially, this capture was robust for both autistic and neurotypical participants, as confirmed by Bayesian analysis. Limitations: While well powered for our research questions, our sample size precluded investigation of the automatic guidance of attention in a diverse group of autistic people (e.g. those with a range of cognitive abilities).Conclusions:Our findings imply that key mechanisms underlying the automatic implementation of top-down attentional goals are intact in autism, challenging theories of reduced top-down control.

Towards the Autonomy: Control Systems for the Ship in Confined and Open Waters.

The concept of the Marine Autonomous Surface Ship (MASS) requires new solutions in many areas: from law, through economics, social sciences, environmental issues to the technology and even ethics. It also plays a central role in the work of numerous research teams dealing with the ship motion control systems. This article presents the results of the experiments with application of the selected control methods in automatic steering of the movement of an autonomous ship in the two regimes: during the maneuvers at low speed (in a harbor confined waters) and during the lake trials in open water conditions. In the first case, multidimensional state controller synthesized with Linear Matrix Inequalities (LMI) algorithms was used, while, in the second case, Model Predictive Control (MPC) control was adopted. The object for which the experiments were carried out was 1:24 scale model of the Liquefied Natural Gas (LNG) carrier. The paper presents also the design of the measurement and control system and the user interface. The experiments were conducted in the natural conditions on the lake. The results of the experiments indicate the fundamental role of the measurement system in the process of controlling an autonomous ship.

An intelligent multiple-articulated rubber-tired vehicle based on automatic steering and trajectory following method

For manually driven rubber trams to track, virtual tracks can easily cause driver fatigue. Therefore, based on visual navigation, an automatic steering and trajectory following method are proposed. First, the vehicle kinematic and dynamic model of the Delight Tram is proposed. Then, the automatic steering and trajectory following methods are introduced, which are based on model prediction control and Ackermann steering theory, respectively. Finally, the effectiveness of the proposed methods has been evaluated via both multi-body dynamic simulations and road tests under various working conditions. The results show that the vehicle has excellent steering and trajectory following ability whether in a transient phase or a steady-state circumference. Furthermore, the steering system can stabilize the vehicle in the whole range of design speed, with a smaller computational cost.

Implementation of a variable-geometry suspension-based steering control system

The paper proposes the implementation of a control system for a variable-geometry suspension to achieve steering functionality. The implementation is performed in a Hardware-in-the-Loop (HiL) simulation environment with a unique suspension test bed. The purpose of the control system is to achieve path-following functionality for the vehicle with automatic steering based on the proposed system. The paper proposes the design and the implementation of a hierarchical control system. The high-level control is responsible for the computation of the requested steering angle, and the low-level control aims to realise the steering angle through the intervention of the linear actuator in the suspension system. The effectiveness of the proposed control system is demonstrated through HiL simulation examples.

A novel robust event‐triggered fault tolerant automatic steering control approach of autonomous land vehicles under in‐vehicle network delay

AbstractThis paper proposes a novel robust event‐triggered fault tolerant automatic steering control strategy for autonomous land vehicles to achieve path tracking and vehicle lateral motion control under in‐vehicle network delay. From the practical point of view, the parameter uncertainties, time delay, and actuator fault are simultaneously introduced to make the designed controller robust to more extensive and challenging driving conditions. A novel polytope reduction and norm‐bounded uncertainty reduction method is used to effectively handle the time‐varying velocity and tire cornering stiffness uncertainties. Then, the uncertain vehicle model can be reconstructed as an uncertain network control system model with time delay and actuator fault. Due to the inevitable time delay and actuator fault, a new cubic absolute‐value Lyapunov function is developed to guarantee the asymptotical stability of the closed control system with the H∞ performance, and the modified project‐based adaptive law is applied to strengthen the fault tolerant ability. Furthermore, a progressive event‐triggered mechanism is proposed for the collaborative design of robust fault tolerant automatic steering controller, which can signally improve the communication resource utilization of the limited bandwidth in‐vehicle network. Finally, the performance comparisons of different controllers are presented. These results prove that the proposed controller can simultaneously guarantee the path tacking performance and lateral dynamics stability, and save the communication resource of the in‐vehicle network.

Advancing automatic guidance in virtual science inquiry: from ease of use to personalization.

This article is in response to the review entitled “Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review” (Zacharia et al. in Educ Technol Res Dev 63(2): 257–302). As COVID-19 alerts us to shift science education to digital when in-person schooling is not viable, one approach to facilitate this shift, as reviewed by Zacharia et al. (2015), is to involve students in computer supported inquiry learning (CoSIL) with appropriate guidance. As CoSIL guidance is critical to student success in CoSIL, Zacharia et al. (2015) contribute to our knowledge by systematically reviewing the forms and the efficacy of such guidance tools that are associated with each phase of scientific inquiry. With such knowledge we may develop decent guidance so that students can experience scientific inquiry virtually as they used to do in-person. Zacharia et al. (2015) indicated that the various guidance tools had increased the ease of use of CoSIL but failed in personalizing CoSIL to individual students. I agree with Zacharia et al. that the personalization of CoSIL guidance is vital. Further, I argue that the emergent machine learning may significantly increase the personalization of CoSIL without burdening teachers. I conclude the essay with suggestions to further investigate the cognitive needs of students in CoSIL and integrate the content, CoSIL, and guidance tools, as a way to move forward the personalization of CoSIL.

Implementation of optimal automatic steering of atomic clock timescale

The problem of physical implementation of atomic timescales maximally adjusted to the national timescale UTC(SU) for working and secondary standards is considered. Results of analysis of stationary process of hydrogen maser timescale automatic steering to UTC(SU) using common view technique based on the signals of global navigational systems are presented. Calculation of Kalman filter and linear quadratic regulator parameters is described. Theoretical and experimental estimations of automatic steering quality characteristics are compared and discussed. The results obtained can be used to improve the accuracy of timescales on working and secondary standards.

Emergency Autonomous Steering Control Research Based on Receding Horizon H∞ Control and Minimax Criteria

Aiming at the problems of model uncertainty, external interference of road conditions, and inherent nonlinearity of lateral movement of autonomous driving vehicles, this paper proposes an effective control technology for an automatic steering system, namely, the receding horizon $H_{\infty }$ control, which has good path tracking performance and can overcome external interference. First, a two-degree-of-freedom vehicle model and a road model are established. Then, the Hamilton equation is established using Pontriagin’s minimum principle, and the optimal control solution and the most serious disturbance are solved based on the minimax principle. Finally, in order to evaluate the receding horizon $H_{\infty }$ autonomous steering control method, lane change simulation and hardware in the loop simulation are developed to describe the effect of receding horizon $H_{\mathrm {\infty }}$ . The simulation results show that the model predictive control (MPC) and linear matrix inequality (LMI) controller will exhibit oscillation and instability under the condition of external interference. To improve the stability, the $H_{\infty }$ controller reduces part of the performance, while the rolling horizon $H_{\mathrm {\infty }}$ strategy can ensure the robustness of automatic steering and has a certain anti-interference performance.

Research on Automatic Tractor Steering System for Weeding Operation by Using 5G Communication and AI Image Recognition

Research on Automatic Tractor Steering System for Weeding Operation by Using 5G Communication and AI Image Recognition

Automatic Steering Control of an Unmanned Transport Truck in Off-road Environments

Automatic Steering Control of an Unmanned Transport Truck in Off-road Environments

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control of autonomous medic robots. Health officials aim to curb the spread of COVID-19 among medical, nursing staff and patients by using intelligent robots. We propose an advanced controller for a service robot to be used in hospitals. This type of robot is deployed to deliver food and dispense medications to individual patients. An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction. These criteria were met by using two controllers simultaneously: A deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative (PID) controller for automatic steering and speed control. © 2021 Tech Science Press. All rights reserved.

Comparing Coupled and Decoupled Steering Interface Designs for Emergency Obstacle Evasion

Automatic evasive steering maneuvers can outperform human-initiated steering maneuvers in emergency situations. A steering interface that decouples the steering wheel from the tires may enhance the efficiency of automatic steering maneuvers by providing full authority to the automation system. Yet, an alternative interface in which the steering wheel remains coupled to the tires has the advantages of enabling the driver to intervene in the event of an automation failure and preventing the human factors issues associated with decoupling the driver. In this paper, we present a driving simulator study with 64 participants where we compared four steering interface design schemes in their ability to enable successful obstacle evasion in emergency scenarios. The steering wheel was either decoupled from the tires and the automation was given full authority, or the steering wheel was coupled to the tires and the automation was provided high, low or no authority. The automation was designed to avoid all obstacles, except for the last one when it failed unexpectedly. Results uncovered a design tradeoff: as the authority of an agent (driver or automation) increases, the protection against the agent’s faults (provided by the other agent) reduces. The results also show that decoupled driving reduced driver’s vigilance and mode awareness and deprived the drivers of the authority required to intervene during automation faults. Coupled driving alleviated these issues but caused driver discomfort when designed with high automation authority and resulted in a larger number of collisions during perfect automation operation when designed with low automation authority.

Computer Vision-Control-Based CNN-PID for Mobile Robot

With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed control. Two controllers were used simultaneously to achieve this. Convolutional neural networks (CNNs) are used for target tracking and trajectory prediction, and a proportional-integral-derivative controller is designed for automatic steering and speed control. This study makes use of a Raspberry PI, which is responsible for controlling the robot car and performing inference using CNN, based on its current image input.

Application of new turbulent method for determining vertical refraction

Abstract This paper is concerned with the study of new turbulent method technique for the determining of vertical refraction when total stations are used. Required measurement accuracy of vertical refraction by conventional methods is extremely difficult due to rapid random changes in the angle of refraction. Geodetic observations are recommended to performing only during periods of indifferent temperature stratification, while the refraction is close to zero and practically unvaried. However, this period is extremely short and its boundaries are not defined, so the inefficiency of all known methods for determining refraction must be attention. The complete liberation of geodetic observations from the influence of turbulent and fluctuation processes in the atmosphere is possible only by directly measuring the angle of refraction at the time of observation. The creation of electronic total stations with automatic guidance to the target allows to successfully solving the problem of determining refraction by a turbulent method. The aim of this work is to study the new method for determining refraction in a turbulent atmosphere. The measurements are performed with a Trimble total station. The obtained results confirm that the accuracy for determining refraction is ~2″, which almost corresponds to the instrumental accuracy of the device used.

Maneuverability of Wheeled Poly-Articulated Agricultural Vehicles: Modeling and Field Testing

HighlightsModeling provides the relationships between path, kinematics, geometry, and towed implements.Linear interpolation allows trajectories to be compared if data recording is random.Correction coefficients can be a solution to compensate for soil resistance.Abstract. Automatic guidance systems and autonomous vehicles require tested methods of path generation to ensure successful maneuvers (such as automatic trajectory correction and headland turn management). In this study, an evolution of Zakin’s kinematic modeling, as applied in the automobile industry, is proposed for an agricultural poly-articulated vehicle (representing a tractor or other type of towing vehicle with one or more towed implements attached with an articulated hitch). Geometry, vehicle ground speed, and angular steering velocity are considered in the generation of maneuvering paths. Based on the specifics of real field conditions (slope, plant residue, resistance due to soil compaction, etc.), the initial model was improved by introducing correction coefficients. An experimental setup is proposed using a tractor with two towed implements and a testing method involving point-to-point path comparison. The modeling method has potential for integrating more complex procedures (such as path generation, geolocation, and following) into the design of a maneuvering management system for agricultural machines, which can contribute to the efficiency of field operations. Keywords: Agricultural vehicle, Headland turn automation, Maneuverability, Modeling, Path generation, Path planning, Poly-articulated vehicle.

Low-cost experimental application of real-time kinematic positioning for increasing the benefits in cereal crops

Traditional agriculture is facing several challenges worldwide such as increased population growth, rapid forestry and urbanization, resource scarcity, climate change, environmental pollution, competition among different markets. Hence, farmers need to improve productivity in order to maintain the output level. This study attempted to evaluate the benefits of using Real-Time Kinematic (RTK) positioning in precision agriculture through a series of real measurements carried out when farming cereals. All farming management actions involved in the cereal crop process (raise fallow, plow, sow, fertilize, mow, and harvest) have been done using an automatic guidance system that has reduced costs. A reduction of 20% has been quantified in the fuel, the amount of fertilizer, the labor costs and the hours of work. Consequently, the environmental impact has been also reduced. An inexpensive system consisting of a reference base station near the field and a mobile unit mounted on the test vehicle has been installed in order to increase the benefits in cereal crops. Global Navigation Satellite System (GNSS) systems including Global Positioning System (GPS), GLONASS, Galileo and Beidou have been used in the analysis. This research serves as a practical guide to implementing a low-cost guidance system to achieve best management practice. Keywords: real-time kinematic, low-cost, positioning, benefits, guidance system, cereal DOI: 10.25165/j.ijabe.20211403.5812 Citation: Tayebi A, Gomez J, Fernández M, de Adana F S, Gutiérrez O. Low-cost experimental application of real-time kinematic positioning for increasing the benefits in cereal crops. Int J Agric & Biol Eng, 2021; 14(3): 194–199.