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.

中文翻译：

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使用时空网络模型对自动引导车辆的后视路径规划

时空网络（TSN）模型已广泛用于自动引导车辆的无碰撞路径规划。但是，现有的TSN模型已在全球范围内规划。全局方法具有计算复杂性，并且在动态环境中无法处理不确定性。为了解决这些局限性，本文提出了一种新的方法，可以将全球规划问题分解为较小的局部规划问题，这些问题将以渐进的方式进行规划。对于局部问题，新的决策变量和约束条件已合并到TSN框架中。进行了广泛的仿真实验，以证明所提出方法的潜力。仿真结果表明，与全局方法相比，该方法具有竞争优势，计算时间大大减少。