TY - GEN
T1 - Frontier-Based Exploration Planning for Amphibious Robot with Gaussian Process Occupancy Map
AU - Li, Bo
AU - Pan, Feng
AU - Zhang, Shi
AU - Song, Xiaofei
AU - Cui, Rongxin
N1 - Publisher Copyright:
© 2023 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2023
Y1 - 2023
N2 - The autonomous observation of amphibious environments necessitates the robot to possess the capability of simultaneous exploration and mapping. In this article, we present an efficient exploration planning framework for amphibious robot. This method utilizes the Gaussian Process Occupancy Map (GPOM) as a representation of the environment to improve map quality, and Rapidly-exploring Random Trees (RRT) as a global frontier detector for rapid detection of frontiers. Our proposed method has been evaluated through extensive simulations and real-world experiments utilizing a wheel-propeller integrated amphibious robot. The experimental results demonstrate that our approach outperforms other methods in terms of exploration efficiency.
AB - The autonomous observation of amphibious environments necessitates the robot to possess the capability of simultaneous exploration and mapping. In this article, we present an efficient exploration planning framework for amphibious robot. This method utilizes the Gaussian Process Occupancy Map (GPOM) as a representation of the environment to improve map quality, and Rapidly-exploring Random Trees (RRT) as a global frontier detector for rapid detection of frontiers. Our proposed method has been evaluated through extensive simulations and real-world experiments utilizing a wheel-propeller integrated amphibious robot. The experimental results demonstrate that our approach outperforms other methods in terms of exploration efficiency.
KW - Autonomous Exploration
KW - Frontier
KW - Gaussian Process Occupancy Map
KW - Rapidly-exploring Random Trees
KW - Wheel-propeller integrated amphibious robot
UR - http://www.scopus.com/inward/record.url?scp=85175539512&partnerID=8YFLogxK
U2 - 10.23919/CCC58697.2023.10240863
DO - 10.23919/CCC58697.2023.10240863
M3 - 会议稿件
AN - SCOPUS:85175539512
T3 - Chinese Control Conference, CCC
SP - 3889
EP - 3894
BT - 2023 42nd Chinese Control Conference, CCC 2023
PB - IEEE Computer Society
T2 - 42nd Chinese Control Conference, CCC 2023
Y2 - 24 July 2023 through 26 July 2023
ER -