Lifting path planning of mobile cranes based on an improved RRT algorithm

Ying Zhou, Endong Zhang, Hongling Guo, Yihai Fang, Heng Li

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


Lifting operations of mobile cranes are one of the commonly-seen and most important activities for prefabrication housing production (PHP) on sites. However, relevant operations are normally based on the experience of operators or project managers, this often leads to low efficiency as well as high accident rate due to dynamic and complex construction sites. Thus, it is important and necessary to develop an appropriate approach to the lifting planning of mobile cranes so as to guide on-site operations. This paper proposes an improved Rapidly-exploring Random Tree (RRT) algorithm for lifting path planning of mobile cranes. Considering the critical role of Nearest Neighbor Search (NNS) in the implementation of RRT algorithm, a novel strategy for searching the nearest neighbor is developed, i.e., Generalized Distance Method and Cell Method. Both methods are tested in simulation-based experiments. The results show that 1) the Generalized distance method not only reduces the search time, but also unifies the unit of distance measurement and clarifies the physical meaning of distance; 2) the Cell method dramatically reduces the traversal range as well as the search time; and 3) both methods improve the quality of lifting path planning of mobile cranes. This improved RRT algorithm enables rapid path planning of mobile cranes in a dynamic and complex construction environment. The outcomes of this research not only contribute to the body of knowledge in spatial path planning of crane lifting operations, but also have the potential of significantly improving efficiency and safety in crane lifting practices.

Original languageEnglish
Article number101376
Number of pages9
JournalAdvanced Engineering Informatics
Publication statusPublished - Oct 2021


  • Lifting path planning
  • Mobile crane
  • Nearest neighbor search
  • Optimization
  • Rapidly-exploring Random Tree (RRT)

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