A Sampling-based Next-Best-View Path Planner for Environment Exploration

A new path planner to explore the surrounding environment algorithm is presented for the aerial robot. This algorithm combines Next-Best-View (NBV) sampling and frontier exploration together to find unknown large areas. We first compute the nearby area, namely the frontier area, which can guide the...

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Bibliographic Details
Published in2023 9th International Conference on Mechatronics and Robotics Engineering (ICMRE) pp. 128 - 132
Main Authors Liu, Qishuai, Jiang, Yufan, Li, Ying
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.02.2023
Subjects
Autonomous aerial vehicles
B-spline
Clustering algorithms
exploration
History
Mechatronics
NBV
Robots
Splines (mathematics)
Trajectory
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Summary:A new path planner to explore the surrounding environment algorithm is presented for the aerial robot. This algorithm combines Next-Best-View (NBV) sampling and frontier exploration together to find unknown large areas. We first compute the nearby area, namely the frontier area, which can guide the exploration direction for the robot roughly, then a node tree representing the NBV of the robot is generated for the robot to follow. Next, a continuous trajectory-generated method is proposed where a B-spline curve representing the robot trajectory can follow these selected viewpoints in the history tree. We evaluate the effect of our algorithm in a simulated environment.
DOI:10.1109/ICMRE56789.2023.10106522