Aiming at Articulated Road Roller (ARR) path planning near the construction area, a novel path planning method using Support Vector Machine (SVM) and Longest Accessible Path with Course Correction (LAP-CC) is proposed. First, the feasible path selected by Goal-Directed RRT (GDRRT) will be repeated 20 times to determine which category the obstacle belongs to, and then the error penalty factor and kernel parameter of SVM are selected by the grid search and cross-validation parameter optimization algorithm. Then, A set of different virtual obstacles are used on both sides of ARR to control the start and end points of the zero-potential decision boundary. Next, Longest Accessible Path (LAP) is presented to search critical turning points on the decision boundary. Finally, Course Correction (CC) corrects ARR course at these critical turning points. Simulation experiments show that the novel path planning method for ARR using SVM and LAP-CC has the advantages of simplicity, feasibility, low computational cost and good repeatability.INDEX TERMS Support vector machine, longest accessible path, course correction, path planning, articulated road roller, virtual obstacles, error penalty factor, kernel parameter.

The special steering characteristics and task complexity of autonomous articulated vehicle (AAV) make it often require multiple forward and backward movements during autonomous driving. In this paper, we present a simple yet effective method, named head correction with fixed wheel position (HC-FWP), for the demand of multiple forward and backward movements. The goal-directed rapid-exploring random tree (GDRRT) algorithm is first used to search for a feasible path in the obstacle map, and then, the farthest node search (FNS) algorithm is applied to obtain a series of key nodes, on which HC-FWP is used to correct AAV heading angles. Simulation experiments with Dynapac CC6200 articulated road roller parameters show that the proposed improved goal-directed rapid-exploring random tree (IGDRRT), consisting of GDRRT, FNS, and HC-FWP, can search a feasible path on maps that require the AAV to move back and forth.

Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System-Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy.

Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.

This article develops a four-level test system for accurately evaluating pavement compaction performance of autonomous articulated vehicles. In the evaluation layer, various performance indicators are evaluated, including the stability, rapidity and accuracy of trajectory tracking, and the ratio of required compaction to actual compaction once and twice and compaction repeatability index when pavement compaction. The guidance and control layer can be described in terms of theory and application. At the theoretical level, the line of sight guidance algorithm and incremental proportional integral control algorithm are introduced to eliminate system control lag. Among them, the best line of sight guidance and incremental proportional integral control parameters are selected by the Elitist strategies genetic algorithm, and the initial parameters are set according to human driving experience initial control parameters. At the application level, the BECKHOFF controller, a kind of programmable logic controller, acts as the main guidance and control unit in the four-level control system, fixed speed is given to the autonomous articulated vehicle by setting the engine speed and transmission gear, and steering wheel angle is adjusted in real time by the BECKHOFF controller. In the sensor level, a simplified sensor configuration is used to reduce overall cost. The comparative simulation results of no controller, the incremental proportional integral controller, line of sight guidance-incremental proportional integral controller with human driving experience initial control parameters, line of sight guidance-incremental proportional integral controller with random initial control parameters, and elitist strategies genetic algorithm-line of sight guidance-incremental proportional integral controller with human driving experience initial control parameters manifest evidently that the proposed elitist strategies genetic algorithm-line of sight guidance-incremental proportional integral controller with human driving experience initial control parameters has almost no steady-state error, no overshoot, and short settling time. Field results show that ratio of required compaction to actual compaction once achieves 100%, ratio of required compaction to actual compaction twice achieves 94.6%, and compaction repeatability index achieves 35%.