Learning-based Fast Path Planning in Complex Environments

Liu, Jinfeng; Li, Baopu; Tingguang, Li,; Chi, Wenzheng; Wang, Jiankun; Meng, Max Q.-H.

doi:10.1109/robio54168.2021.9739261

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sampling-based method find the feasibility of the path through collision detections, avoiding the detailed expression of the environment. Sampling-based algorithm connects all of the feasible nodes and find a start-to-goal feasible path based on it [12][13]. The most widely used sampling-based approaches are probabilistic roadmaps (PRM) and random-exploring trees (RRT).…”

Section: Sampling-based Methodsmentioning

confidence: 99%

Research on robot path planning methods

Chen,

Jiang

2023

ACE

View full text Add to dashboard Cite

Mobile robots are used extensively across a variety of industries and fields. How to discover a start-to-end path without colliding has become a hot topic in recent years due to the complexity and uncertainty of the workplace. In various environments, a path planning technique should demonstrate high efficiency and speed. And this can reduce the energy consumption of the robots and greatly increase their working efficiency. This paper will conclude the presently popular path planning algorithm. Based on the different features of these algorithms, they are divided into three types: traditional path planning algorithm, neural-work-based algorithm, and sampling-based algorithm. Based on the new papers in these years, detailed introduction of the algorithms and their variants will be given. At the end of the paper, the thesis is summarized and the future research trend is prospected.

show abstract

Section: Sampling-based Methodsmentioning

confidence: 99%