摘要
随着智能交通系统的快速发展,自主导航路径规划技术变得尤为关键。无人快递车作为新兴的技术手段,其自主导航系统的设计与实现是实现高效、低成本物流配送的关键。然而,传统人工势场法(Artificial Potential Field, APF)在路径规划中存在目标不可达和易陷入局部极小值的问题,限制了其在无人快递车导航系统中的应用。为解决这一问题,本文提出了一种改进的人工势场法,通过引入随机化算法思想,优化斥力场模型,增强了无人快递车在复杂环境中的避障能力和路径搜索效率。改进后的算法不仅提高了路径规划的全局优化性能,还增强了算法的鲁棒性,使无人快递车能够在面对动态环境时,快速找到安全、高效的配送路径。通过仿真实验,验证了所提方法在减少节点扩展、缩短规划时间以及提高路径平滑度方面的有效性。
With the rapid development of intelligent transportation systems,autonomous navigation path planning technology has become increasingly critical.As a novel technological approach,the design and implementation of an autonomous navigation system for unmanned delivery vehicles are key to achieving efficient and cost-effective logistics delivery.However,the traditional Artificial Potential Field(APF)method encounters issues with unreachable goals and a tendency to fall into local minima during path planning,which limits its application in unmanned delivery vehicle navigation systems.To address these issues,this paper proposes an improved artificial potential field method that enhances the repulsive field model by introducing the concept of randomized algorithms,thereby enhancing the obstacle avoidance capabilities and path search efficiency of unmanned delivery vehicles in complex environments.The improved algorithm not only enhances the global optimization performance of path planning but also strengthens the robustness of the algorithm,enabling unmanned delivery vehicles to quickly find safe and efficient delivery paths in response to dynamically changing environments.Simulation experiments have confirmed the effectiveness of the proposed method in reducing node expansion,shortening planning time,and improving path smoothness.
作者
张雨辰
周民
马思豪
ZHANG Yu-chen;ZHOU Min;MA Si-hao(School of Computer and Software,Nanyang Institute of Technology,Nanyang 473004,China)
出处
《南阳理工学院学报》
2024年第4期78-82,95,共6页
Journal of Nanyang Institute of Technology
基金
南阳市科技攻关项目(23KJGG009、23KJGG014)
南阳理工学院2023年度教育教学改革研究与实践项目(NIT2023JY-163)
河南省本科高校2023年课程思政项目。
关键词
无人快递车
智能物流
路径规划
自主导航
unmanned delivery vehicles
intelligent logistics
path planning
autonomous navigation
