基于 MPC 的 AGV 运动控制,改进 A* 和人工势场

Shaosong Li, Qingbin Zhou, Junchen Jiang, Xiaohui Lu, Zhixin Yu
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引用次数: 0

摘要

如今,自动导引车(AGV)被广泛用于车间的运输和检测任务。A*和人工势场(APF)是用于 AGV 路径规划的经典算法。然而,这些算法仍无法满足实际生产需求,也无法避免遇到障碍物时的停顿,从而导致过多的能耗和不必要的停顿。本文提出了一种改进的 A* 算法,以减少路径长度并提高效率。在此基础上,设计了一种由改进的 APF 和非线性模型预测控制(NMPC)组成的集成融合策略,用于避免碰撞和路径跟踪控制。提出的算法在仿真和激光制导的真实自动制导车辆实验平台上进行了测试。实验结果证明,所提出的算法在复杂工作环境下具有良好的跟踪性能。
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MPC-based motion control of AGV with improved A* and artificial potential field
Nowadays, automatic guided vehicles (AGV) are extensively utilized for transportation and inspection tasks in workshops. The A* and artificial potential field (APF) are classic algorithms employed for path planning of AGVs. However, these algorithms still fail to meet the actual production needs and cannot avoid stuttering while encountering obstacles, leading to excessive energy consumption and unnecessary pause. In the paper, an improved A* algorithm is proposed to reduce route length and improving efficiency. On this basis, an integrated fusion strategy consisting of improved APF and nonlinear model predictive control (NMPC) is designed for collision avoidance and path tracking control. The proposed algorithm is tested both on simulation and a laser-guided real automatic guided vehicle experimental platform. Experimental results prove that the proposed algorithm has a great tracking performance under complex workplace.
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来源期刊
CiteScore
4.40
自引率
17.60%
发文量
263
审稿时长
3.5 months
期刊介绍: The Journal of Automobile Engineering is an established, high quality multi-disciplinary journal which publishes the very best peer-reviewed science and engineering in the field.
期刊最新文献
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