RoA-Planner: Rotatable Area-Based Path Planner in Dense Spaces
Yeongwoo Son, Hyunyong Lee, Hansol Kang, Jiman Park, Seongwon Nam, Jaeyoung Oh, Bumsu Yi, Junha Song, Sooyeon Choi,
Bogeun Kim, Daegeun Song and Hyouk Ryeol Choi*,
Fellow
,
IEEE
{ywson96, choihyoukryeol}@gmail.com
IEEE Transactions on Automation Science and Engineering (T-ASE) 2025
LOCAL MOTION BETWEEN ROAS
β’Our approach regards local motion as a relationship between
two RoAs, πΉ
π
β πΉ
π
.
β’Local motion consists of 1) rotation, and 2) subsequent
translation.
β’The Feasibility of local motion is determined by area and edge
conditions.
Area condition
βTwo RoAs must have angular intersections.β
Edge condition
βThe distance between two RoAs must be below ο½aximum length.β
MOTIVATION
β’Path planning in dense environments is challenging for an
asymmetric rectangular robot.
β’Existing planning methods are either conservative, aggressive,
or computationally expensive due to a large search space.
We introduce Rotatable Area (RoA),
the range of yaw angles from π
π
to π
π
over which the robot can safely rotate
at position (π₯, π¦)
π
without colliding with
obstacles.
πΉ = (π, π,π
π
, π
π
)
FRAMEWORK
β’The overall framework consists of two stages: the mapping
and the planning stage.
β’Mapping: We decompose the environment into a quadtree
and generate a RoA-Map, indicating the RoA in each cell.
β’Planning: We develop an A* algorithm for RoA-based graph,
which regards nodes as RoAs and edges as feasible motions
between two RoAs.
Motion space
must be contained in
safe space.
π
π
β π
π
1)
Safe space
must be connected.
2)
Safe width
must be larger than
motion width
.
Maximum length
Calculation
DEMONSTRATION
Simulation
Real-world
β’Our method demonstrates fast, accurate, and safe navigation
even in highly cluttered and narrow spaces.
β’Our method enables AiDIN-VIII to achieve robust navigation
across various real-world scenarios.
MORE INFORMATION
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2
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β RoA-Planner Journal
β‘ 3D RoA-Planner Journal
β’ Personal Website
