A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

被引：59

作者：

Wang, Hao ^{[1
,2
]}

Li, Guoqing ^{[1
,2
]}

Hou, Jie ^{[1
,2
]}

Chen, Lianyun ^{[1
,3
]}

Hu, Nailian ^{[1
,2
]}

机构：

[1] Univ Sci & Technol Beijing, Coll Civil & Resource Engn, Beijing 100083, Peoples R China

[2] Key Lab Efficiency Min & Safety Met Mines, Minist Educ, Beijing 100083, Peoples R China

[3] Shandong Gold Grp Co Ltd, Jinan 250014, Peoples R China

来源：

ELECTRONICS | 2022年 / 11卷 / 03期

基金：

中国国家自然科学基金;

关键词：

underground intelligent vehicles; path planning; RRT* algorithm; articulated vehicles; unmanned driving; OPTIMIZATION; ROBOT; UAV; TECHNOLOGY; SYSTEMS;

D O I：

10.3390/electronics11030294

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

引用

页数：18

共 45 条

[1]

Altafini C, 1999, ICRA '99: IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-4, PROCEEDINGS, P3020, DOI 10.1109/ROBOT.1999.774056

[2] Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control [J].

Bai, Guoxing ;

Liu, Li ;

Meng, Yu ;

Luo, Weidong ;

Gu, Qing ;

Ma, Baoquan .

APPLIED SCIENCES-BASEL, 2019, 9 (07)

[3]

Bai Y., 2017, P 2017 IEEE 20 INT C, P1

[4]

Bearne G., 2014, Alum. Int. Today, V26, P15

[5] Development of an optimal state transition graph for trajectory optimisation of dynamic systems by application of Dijkstra's algorithm [J].

Biyela, Philani ;

Rawatlal, Randhir .

COMPUTERS & CHEMICAL ENGINEERING, 2019, 125 :569-586

[6] Coal Mine Safety Intelligent Monitoring Based on Wireless Sensor Network [J].

Chen, Wei ;

Wang, Xuzhou .

IEEE SENSORS JOURNAL, 2021, 21 (22) :25465-25471

[7] Radiation search operations using scene understanding with autonomous UAV and UGV [J].

Christie, Gordon ;

Shoemaker, Adam ;

Kochersberger, Kevin ;

Tokekar, Pratap ;

McLean, Lance ;

Leonessa, Alexander .

JOURNAL OF FIELD ROBOTICS, 2017, 34 (08) :1450-1468

[8]

CHUGH YP, 1981, J S AFR I MIN METALL, V81, P57

[9] Mutual Information-Based Multi-AUV Path Planning for Scalar Field Sampling Using Multidimensional RRT* [J].

Cui, Rongxin ;

Li, Yang ;

Yan, Weisheng .

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (07) :993-1004

[10]

Dang T, 2019, IEEE INT C INT ROBOT, P3105, DOI [10.1109/iros40897.2019.8968151, 10.1109/IROS40897.2019.8968151]

← 1 2 3 4 5 →