Robot path planning in narrow passages based on improved PRM method

IF 2.3 4区计算机科学 Q3 ROBOTICS Intelligent Service Robotics Pub Date : 2024-02-27 DOI:10.1007/s11370-024-00527-4

引用次数: 0

Abstract

Probabilistic roadmap (PRM) method has been shown to perform well in robot path planning. However, its performance degrades when the robot needs to pass through narrow passages. To solve this problem, an improved PRM method with hybrid uniform sampling and Gaussian sampling is proposed in this paper. With the proposed method, the robot can improve the success rate and efficiency of path planning in narrow passages. Firstly, the narrow-passage-aware Gaussian sampling method is developed for narrow passages. Combining uniform sampling globally, the new sampling strategy can increase the sampling density at the narrow passages and reduce the redundancy of the samples in the wide-open regions. Then, we propose to use density-based clustering method to achieve accurate identification of narrow channels by removing the noise points. Next, graph search algorithm is used to search the shortest path from the start point to the goal point. Finally, simulations are carried out to evaluate the validity of the proposed method. Results show that the improved PRM method is more effective for path planning with narrow passages.

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基于改进的 PRM 方法的狭窄通道机器人路径规划

摘要概率路线图（PRM）方法在机器人路径规划中表现出色。然而，当机器人需要通过狭窄通道时，该方法的性能就会下降。为了解决这个问题，本文提出了一种混合均匀采样和高斯采样的改进型 PRM 方法。采用这种方法，机器人可以提高在狭窄通道中路径规划的成功率和效率。首先，针对狭窄通道开发了窄通道感知高斯采样方法。结合全局均匀采样，新的采样策略可以提高狭窄通道的采样密度，减少开阔区域的冗余采样。然后，我们提出使用基于密度的聚类方法，通过去除噪声点来实现狭窄通道的精确识别。接着，使用图搜索算法搜索从起点到目标点的最短路径。最后，通过仿真来评估所提出方法的有效性。结果表明，改进后的 PRM 方法对狭窄通道的路径规划更为有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Service Robotics ROBOTICS-

CiteScore

5.70

自引率

4.00%

发文量

期刊介绍： The journal directs special attention to the emerging significance of integrating robotics with information technology and cognitive science (such as ubiquitous and adaptive computing,information integration in a distributed environment, and cognitive modelling for human-robot interaction), which spurs innovation toward a new multi-dimensional robotic service to humans. The journal intends to capture and archive this emerging yet significant advancement in the field of intelligent service robotics. The journal will publish original papers of innovative ideas and concepts, new discoveries and improvements, as well as novel applications and business models which are related to the field of intelligent service robotics described above and are proven to be of high quality. The areas that the Journal will cover include, but are not limited to: Intelligent robots serving humans in daily life or in a hazardous environment, such as home or personal service robots, entertainment robots, education robots, medical robots, healthcare and rehabilitation robots, and rescue robots (Service Robotics); Intelligent robotic functions in the form of embedded systems for applications to, for example, intelligent space, intelligent vehicles and transportation systems, intelligent manufacturing systems, and intelligent medical facilities (Embedded Robotics); The integration of robotics with network technologies, generating such services and solutions as distributed robots, distance robotic education-aides, and virtual laboratories or museums (Networked Robotics).