Receding horizon path planning of automated guided vehicles using a time-space network model
Receding horizon path planning of automated guided vehicles using a time-space network model
复制标题
基于时空网络模型的自动导引车后退地平线路径规划
DOI：
10.1002/oca.2654
复制
发表时间：
2020
影响因子：
1.8
通讯作者：
Hua Xuan
中科院分区：
文献类型：
--
作者：
Jianbin Xin;Liuqian Wei;Dongshu Wang;Hua Xuan
时空网络（TSN）模型已被广泛用于自动引导车辆的无碰撞路径规划。然而，现有的TSN模型是在全球范围内规划的。全局方法具有计算复杂性，并且在动态环境中不能处理不确定性。为了解决这些局限性，本文提出了一种新的方法来分解的全局规划问题成更小的局部规划问题，这是计划在一个滚动的地平线的方式。对于局部问题，新的决策变量和约束被纳入TSN框架。进行了大量的仿真实验，以显示所提出的方法的潜力。仿真结果表明，与全局方法相比，该方法具有较好的性能，且计算时间大大减少。
Time‐space network (TSN) models have been widely used for collision‐free path planning of automated guided vehicles. However, existing TSN models are planned globally. The global method suffers from computational complexity and uncertainties cannot be dealt with in the dynamic environment. To address these limitations, this article proposes a new methodology to decompose the global planning problem into smaller local planning problems, which are planned in a receding horizon way. For the local problem, new decision variables and constraints are incorporated into the TSN framework. Extensive simulation experiments are carried out to show the potential of the proposed methodology. Simulation results show that the proposed method obtains competitive performances and computational times are considerably reduced, compared with the global method.