Research on path planning method for staircase turning platforms

For indoor path planning, it is essential to account for inter-floor navigation requirements. The unmanned vehicle must navigate the turning platform when ascending or descending stairs. These narrow platforms, combined with the vehicle's large size, increase the risk of collisions and scrapes while turning. To address this issue, an improved A* algorithm is proposed. This method first expands obstacles when generating a 2D grid map and improves the cost function of the A* algorithm by introducing the reciprocal distance between node n and the handrail of the turning platform, to avoid obstacles as well as adding a turning cost function to make the planned path smoother. By comprehensively considering these costs, a safer and more feasible path can be planned. Next, the ‘arc collision detection method’ is used to calculate whether the arc of the vehicle's diagonal during a turn overlapped with surrounding obstacles, effectively preventing the vehicle from colliding or scraping against obstacles while turning. Then, Bezier curve is used to smooth the path, solving the problem of sharp turns that are unfavorable for vehicle movement. Simulation experiments demonstrate that, with this method, the unmanned vehicle can safely navigate stair turning platforms, achieving shorter path lengths and planning times compared to traditional algorithms..

Authors

Institutions

Publication Details

Published
2025-03-14
DOI
https://doi.org/10.1109/itoec63606.2025.10969064
Primary Topic
Simulation and Modeling Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on path planning method for staircase turning platforms

Yuwei Ma, Weichao Wu, Ailin Niu
Simulation and Modeling Applications
article

Research on path planning method for staircase turning platforms

Yuwei Ma, Weichao Wu, Ailin Niu
article en

Abstract

For indoor path planning, it is essential to account for inter-floor navigation requirements. The unmanned vehicle must navigate the turning platform when ascending or descending stairs. These narrow platforms, combined with the vehicle's large size, increase the risk of collisions and scrapes while turning. To address this issue, an improved A* algorithm is proposed. This method first expands obstacles when generating a 2D grid map and improves the cost function of the A* algorithm by introducing the reciprocal distance between node n and the handrail of the turning platform, to avoid obstacles as well as adding a turning cost function to make the planned path smoother. By comprehensively considering these costs, a safer and more feasible path can be planned. Next, the ‘arc collision detection method’ is used to calculate whether the arc of the vehicle's diagonal during a turn overlapped with surrounding obstacles, effectively preventing the vehicle from colliding or scraping against obstacles while turning. Then, Bezier curve is used to smooth the path, solving the problem of sharp turns that are unfavorable for vehicle movement. Simulation experiments demonstrate that, with this method, the unmanned vehicle can safely navigate stair turning platforms, achieving shorter path lengths and planning times compared to traditional algorithms..

Beijing Institute of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 91%
Simulation and Modeling Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Research on path planning method for staircase turning platforms — Yuwei Ma, Weichao Wu, et al. · (2025) | TGRS Research Map | TGRS