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

doi:10.1109/TIV.2023.3312813

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas
	Yang, Qingyuan 1,2; Ai, Yunfeng 2,3; Teng, Siyu 4; Gao, Yu 2; Cui, Chenglin 1,2; Tian, Bin 1,2,5; Chen, Long 2,6
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-10-01
卷号	8 期号:10 页码:4319-4330
关键词	Mining industry autonomous automobiles intelligent vehicles
ISSN号	2379-8858
DOI	10.1109/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.
资助项目	National Key R&D Program of China[2022YFB4703700]
WOS关键词	COLLISION-AVOIDANCE ; GENERATION ; VEHICLES ; SYSTEM
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
内容类型	期刊论文
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.