Multi-objective cost-to-go functions on robot navigation in dynamic environments
Multi-objective cost-to-go functions on robot navigation in dynamic environments
复制标题
动态环境中机器人导航的多目标成本函数
DOI：
10.1109/iros.2015.7353914
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Sanfeliu
中科院分区：
文献类型：
--
作者：
G. Ferrer;A. Sanfeliu
在我们以前的工作中[1]，我们介绍了预期Kinodynamic规划（AKP）：一种动态城市环境中的机器人导航算法，旨在最大限度地减少对附近行人的干扰。在本文中，我们保持了AKP的所有优点，我们克服了以前的局限性，提出了新的贡献，我们的方法。首先，我们提出了一个多目标的成本函数，考虑不同的和独立的标准和一个适定的过程，以建立一个联合成本函数，以选择最佳路径，然后，我们改进了规划树的建设，通过引入成本去函数，将被证明是优于经典的欧几里德距离的方法。为了实现真实的时间计算，我们使用了一个引导启发式，大大加快了这个过程。大量的仿真和真实的实验证明了AKP算法的有效性。
In our previous work [1] we introduced the Anticipative Kinodynamic Planning (AKP): a robot navigation algorithm in dynamic urban environments that seeks to minimize its disruption to nearby pedestrians. In the present paper, we maintain all the advantages of the AKP, and we overcome the previous limitations by presenting novel contributions to our approach. Firstly, we present a multi-objective cost function to consider different and independent criteria and a well-posed procedure to build a joint cost function in order to select the best path. Then, we improve the construction of the planner tree by introducing a cost-to-go function that will be shown to outperform a classical Euclidean distance approach. In order to achieve real time calculations, we have used a steering heuristic that dramatically speeds up the process. Plenty of simulations and real experiments have been carried out to demonstrate the success of the AKP.