K-order surrounding roadmaps path planner for robot path planning
Issue Date
2014-09Subjects
robot path planninghigh-quality roadmaps
sample-based path planning
path planning
robotics
K-order
Metadata
Show full item recordAbstract
Probabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.Citation
Li, Y., Li, D., Maple, C., Yue, Y., Wang, Z. and Oyekan, J.(2014) 'K-Order Surrounding Roadmaps Path Planner for Robot Path Planning' Journal of Intelligent & Robotic Systems, Vol 75 (3-4), pp493-516.Publisher
SpringerAdditional Links
http://link.springer.com/10.1007/s10846-013-9861-3Type
ArticleLanguage
enISSN
0921-02961573-0409
ae974a485f413a2113503eed53cd6c53
10.1007/s10846-013-9861-3