A robust path tracking algorithm for connected and automated vehicles under i-VICS

Ge, Jingwei; Pei, Huaxin; Yao, Danya; Zhang, Yi

doi:10.1016/j.trip.2021.100314

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…The most prevalent path-tracking algorithms are pure pursuit [28], Stanley [29], Least Quadratic Regulator (LQR) [30], and Model Predictive Control (MPC) [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

et al. 2022

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Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.

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“…The most prevalent path-tracking algorithms are pure pursuit [28], Stanley [29], Least Quadratic Regulator (LQR) [30], and Model Predictive Control (MPC) [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

et al. 2022

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“…While KF can be used as an offline or online algorithm depending on what the optimization objective is (i.e., what the system model from Equations ( 39) and ( 40) represents), in practical applications concerning autonomous vehicles, it is typically used in an online fashion. Although not always in the LEO context, the most common examples of KF implementations in the field of autonomous vehicles are sensor fusion [169] and/or trajectory tracking [170]. The method typically does not consume significant resources from the system and adds negligible delay to the operation; thus, it remains a highly utilized method, although not always for optimization but rather as a method for estimation, tracking, etc.…”

Section: Algorithm 5 Kfmentioning

confidence: 99%

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Çelikbilek

Saleem

Ferre

et al. 2022

Sensors

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Future autonomous transportation is one of the most demanding application areas in terms of connectivity, as it has to simultaneously meet stringent criteria that do not typically go hand in hand, such as high throughput, low latency, high coverage/availability, high positioning and sensing accuracies, high security and robustness to interferences, etc. In order to meet the future demands of challenging applications, such as applications relying on autonomous vehicles, terrestrial networks are no longer sufficient and are to be augmented in the future with satellite-based networks. Among the emerging satellite networks, Low Earth Orbit (LEO) networks are able to provide advantages over traditional Medium Earth Orbit (MEO) and Geo-Stationary Earth Orbit (GEO) networks in terms of signal latency, cost, and performance. Nevertheless, several challenges exist in LEO system design, which have not been fully addressed in the existing literature. In particular, the problem of LEO-system optimization of design parameters is a multi-dimensional problem with many aspects to be considered. This paper offers a comprehensive survey of the LEO-system design parameters, of the challenges in LEO system design process, and of the optimization methods for satellite communication, positioning, and sensing applications, as well as a summarizing discussion on the design considerations for LEO-based networks to support future autonomous transportation.

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“…The basic machinery refers to the tractor where the CDI will be applied. Based on the above and in relation to the SPPA cycle, the basic elements for the automated steering of tractors are sensors for object detection, localization and mapping [52][53][54], path planning algorithms [55], path tracking and steering control [56]. Table 1 includes a list of the aforementioned basic elements for an autonomous self-steering tractor.…”

Section: Basic Modelingmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

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A robust path tracking algorithm for connected and automated vehicles under i-VICS

Cited by 13 publications

References 13 publications

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Computer Vision in Self-Steering Tractors

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