Multiobjective Scheduling Strategy With Genetic Algorithm and Time-Enhanced A* Planning for Autonomous Parking Robotics in High-Density Unmanned Parking Lots

被引:29
作者
Chen, Guang [1 ]
Hou, Jing [1 ]
Dong, Jinhu [1 ]
Li, Zhijun [2 ]
Gu, Shangding [3 ]
Zhang, Bo [4 ]
Yu, Junwei [1 ]
Knoll, Alois [5 ]
机构
[1] Tongji Univ, Dept Automot Engn, Shanghai 200092, Peoples R China
[2] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China
[3] Wuhan Univ Technol, Wuhan 430070, Peoples R China
[4] Shanghai Westwell Informat & Technol Co Ltd, Autonomous Driving Grp, Shanghai 200050, Peoples R China
[5] Tech Univ Munich, D-85748 Munich, Germany
基金
欧盟地平线“2020”;
关键词
Task analysis; Robots; Path planning; Automobiles; Scheduling; Genetic algorithms; Scheduling algorithms; Genetic algorithm; high-density automatic parking; multirobot systems; optimal scheduling algorithm;
D O I
10.1109/TMECH.2020.3023261
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the process of urbanization, the problem of insufficient parking spaces has become prominent. Adopting a high-density parking lot with parking robots can greatly improve the land utilization rate of the parking lot. This article tackles the multiple parking robots scheduling problem of high-density layout parking lots, including task execution sequence decision, robot allocation, and cooperative path planning. First, we mathematically describe the parking robot scheduling problem. Existing approximation algorithms are often far from the optimal solution. This article proposes an improved genetic algorithm and a time-enhanced A* path planning algorithm for high-density parking lots. The improved genetic algorithm can efficiently search task execution sequence and robot allocation and converge to the optimal solution even in large-scale complex scenarios. Meanwhile, the time-enhanced A* algorithm takes a new dimension "the time" into consideration, together with the distance, and security factors, to solve the multi-parking-robot path planning problem. Simulation experiments show that our algorithm can improve scheduling performance in many aspects such as task execution time, driving distance, and security in large-scale high-density parking lots. This article provides an efficient and convenient scheduling solution for the implementation of the high-density unmanned parking lot.
引用
收藏
页码:1547 / 1557
页数:11
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