Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

IF 2.1 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Machines Pub Date : 2023-11-25 DOI:10.3390/machines11121050

Konchanok Vorasawad, Myoungkuk Park, Changwon Kim

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引用次数: 0

Abstract

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.

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智能仓库中自主叉车的高效导航和运动控制：LSPB 轨迹规划与 MPC 实现

智能工厂和仓库的兴起开创了智能制造时代，而自主机器人在其中扮演着举足轻重的角色。本研究的重点是改进自主叉车在仓库环境中的导航和控制。它引入了一种创新方法，将改进的抛物线混合线性段（LSPB）轨迹规划与模型预测控制（MPC）相结合，确保机器人高效、安全地移动。为了验证我们提出的路径规划方法的性能，我们在各种场景（包括矩形和类似仓库的环境）中进行了基于 MATLAB 的仿真，以证明所提方法的可行性和有效性。结果表明，在自动化仓库中使用基于 Mecanum 轮的机器人是可行的。此外，为了证明所提出的控制算法性能优越，还将导航结果与使用 PID 控制作为底层控制器的系统性能进行了比较。通过提供优化路径规划方法，我们的研究提高了 Mecanum 轮式机器人在自动化仓储系统等实际应用中的运行效率和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Machines Multiple-

CiteScore

3.00

自引率

26.90%

发文量

1012

审稿时长

11 weeks

期刊介绍： Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.