Samaniego Ricardo, Rodríguez Rodrigo, Vázquez Fernando, López Joaquín
Imatia Innovation S.L., Galileo Galilei 64, 15008 A Coruña, Spain.
Department of Systems Engineering and Automation, School of Industrial Engineering, University of Vigo, 36310 Vigo, Spain.
Sensors (Basel). 2020 Nov 29;20(23):6821. doi: 10.3390/s20236821.
Motion planning and control for articulated logistic vehicles such as tugger trains is a challenging problem in service robotics. The case of tugger trains presents particular difficulties due to the kinematic complexity of these multiarticulated vehicles. Sampling-based motion planners offer a motion planning solution that can take into account the kinematics and dynamics of the vehicle. However, their planning times scale poorly for high dimensional systems, such as these articulated vehicles moving in a big map. To improve the efficiency of the sampling-based motion planners, some approaches combine these methods with discrete search techniques. The goal is to direct the sampling phase with heuristics provided by a faster, precociously ran, discrete search planner. However, sometimes these heuristics can mislead the search towards unfeasible solutions, because the discrete search planners do not take into account the kinematic and dynamic restrictions of the vehicle. In this paper we present a solution adapted for articulated logistic vehicles that uses a kinodynamic discrete planning to bias the sampling-based algorithm. The whole system has been applied in two different towing tractors (a tricycle and a quadricycle) with two different trailers (simple trailer and synchronized shaft trailer).
对于诸如牵引车列车之类的铰接式物流车辆而言，运动规划与控制是服务机器人技术中的一个具有挑战性的问题。由于这些多关节车辆的运动学复杂性，牵引车列车的情况存在特殊困难。基于采样的运动规划器提供了一种能够考虑车辆运动学和动力学的运动规划解决方案。然而，对于高维系统，例如在大地图中移动的这些铰接式车辆，它们的规划时间扩展性较差。为了提高基于采样的运动规划器的效率，一些方法将这些方法与离散搜索技术相结合。目标是利用由更快、更早运行的离散搜索规划器提供的启发式方法来指导采样阶段。然而，有时这些启发式方法可能会将搜索引向不可行的解决方案，因为离散搜索规划器没有考虑车辆的运动学和动力学限制。在本文中，我们提出了一种适用于铰接式物流车辆的解决方案，该方案使用运动动力学离散规划来使基于采样的算法产生偏差。整个系统已应用于两种不同的牵引车（一辆三轮车和一辆四轮车）以及两种不同的拖车（简单拖车和同步轴拖车）。
