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Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas

文献类型：期刊论文

作者Yang, Qingyuan1,2; Ai, Yunfeng2,3; Teng, Siyu4; Gao, Yu2; Cui, Chenglin1,2; Tian, Bin1,2,5; Chen, Long2,6
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2023-10-01
卷号8期号:10页码:4319-4330
ISSN号2379-8858
关键词Mining industry autonomous automobiles intelligent vehicles
DOI10.1109/TIV.2023.3312813
通讯作者Ai, Yunfeng(aiyunfeng@ucas.ac.cn)
英文摘要Cooperative trajectory planning for autonomous vehicles has garnered significant attention in structured environments, but corresponding methodologies for unstructured environments remains relatively underexplored. The unloading area, an integral component of open-pit mines, exemplifies a quintessential unstructured environment. Implementing cooperative planning for autonomous mining trucks (AMTs) within these unloading areas is crucial as the optimization of processes in these areas substantially enhances the overarching safety, productivity, and cost-effectiveness of mining operations. Hence, enhancing the operational efficiency of AMTs in the unloading area can considerably elevate productivity levels of open-pit mines. This article focuses on the real-time cooperative trajectory planning problem for AMTs in such areas, which is challenging due to i) small and irregular space ii) complex operations iii) need for path stability and speed flexibility. We propose a decoupled multi-vehicle trajectory planning (MVTP) method that decomposes trajectory planning into path planning and speed planning. Specifically, we present driving behavior enhanced path planning and sequential real-time cooperative speed planning methods. Our method is compared with several state-of-the-art MVTP methods and proves to be both secure and efficient.
WOS关键词COLLISION-AVOIDANCE ; GENERATION ; VEHICLES ; SYSTEM
资助项目National Key R&D Program of China[2022YFB4703700]
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001109113000001
资助机构National Key R&D Program of China
源URL[http://ir.ia.ac.cn/handle/173211/55113]  
专题多模态人工智能系统全国重点实验室
通讯作者Ai, Yunfeng
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Waytous Inc, Qingdao 266109, Peoples R China
3.Univ Chinese Acad Sci, Artificial Intelligence Dept, Beijing 100049, Peoples R China
4.Hong Kong Baptist Univ, Hong Kong 999077, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Sun Yat Sen Univ, Sch Comp Sci & Engn, Guangzhou 510275, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Qingyuan,Ai, Yunfeng,Teng, Siyu,et al. Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(10):4319-4330.
APA Yang, Qingyuan.,Ai, Yunfeng.,Teng, Siyu.,Gao, Yu.,Cui, Chenglin.,...&Chen, Long.(2023).Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(10),4319-4330.
MLA Yang, Qingyuan,et al."Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.10(2023):4319-4330.

入库方式： OAI收割

来源：自动化研究所

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