HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots
HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots
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DOI：
10.1109/access.2020.3043333
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Kobayashi, Yukinori
中科院分区：
文献类型：
--
作者：
Ravankar, Ankit A.;Ravankar, Abhijeet;Kobayashi, Yukinori
路径规划和导航是机器人技术中的一个重要问题，尤其是对于在复杂环境中工作的移动的机器人。基于采样的规划器，如概率路线图（PRM）已被广泛用于不同的机器人应用。然而，由于PRM中节点的随机抽样，它遭受窄通道问题，产生不连通图。该问题通过增加节点数量来解决，但计算成本较高，影响实时性能。针对这一问题，本文提出了一种改进的基于采样的移动的机器人导航路径规划方法。该方法采用分层混合概率路线图（PRM）和人工势场（APF）方法进行全局规划。我们使用了一种分解方法的节点分布，使用地图分割，以产生高，低潜力的地区，并提出了一种方法，减少分散的样本集在路线图建设。我们的方法产生更好的目标规划查询与一个更小的图，是计算效率比传统的PRM。所提出的规划称为混合潜力的概率路线图（HPPRM）是一种改进的抽样方法的成功率和计算成本。此外，我们提出了一种方法，反应局部运动规划中存在的静态和动态障碍物的环境。所提出的方法的优点是，它可以避免局部极小值，并成功地生成计划，在复杂的地图，如狭窄的通道和错误陷阱的情况下，否则难以为传统的基于样本的方法。我们在模拟和真实的环境中的局部和全局规划的实验表明，我们的方法的有效性。结果表明，所提出的HPPRM是有效的自主移动的机器人在复杂环境中的导航。所提出的方法的成功率是高于95%的本地和全球规划。
Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments. Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications. However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph. The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performance. To address this issue, in this paper, we propose an improved sampling-based path planning method for mobile robot navigation. The proposed method uses a layered hybrid Probabilistic Roadmap (PRM) and the Artificial Potential Field (APF) method for global planning. We used a decomposition method for node distribution that uses map segmentation to produce regions of high and low potential, and propose a method of reducing the dispersion of sample set during the roadmap construction. Our method produces better goal planning queries with a smaller graph and is computationally efficient than the traditional PRM. The proposed planner called the Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method with respect to success rate and calculation cost. Furthermore, we present a method for reactive local motion planning in the presence of static and dynamic obstacles in the environment. The advantage of the proposed method is that it can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods. We show the validity of our method with experiments in simulation and real environments for both local and global planning. The results indicate that the proposed HPPRM is effective for autonomous mobile robot navigation in complex environments. The success rate of the proposed method is higher than 95% both for local and global planning.