TY - GEN
T1 - TMBC
T2 - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
AU - Wang, Qihang
AU - Chen, Xiaopeng
AU - Ullah, Zakir
AU - Tang, Shengquan
AU - Yu, Mingming
AU - Xu, Peng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The ability to plan online paths, that passes through points of interest for coverage of an unknown environment is essential to robot autonomy. Existing state of the art coverage path planning techniques for active exploration (Frontier and Next-Best-View) are occupancy grid maps based, which do not scale well in large area environments, suffer from long term drift and are not robust at large values of sensor range noise, not address robot navigation safety. We proposed a Coverage Path Planning (CPP) algorithm, which is based on a coordinate system based topological map of the admissible locations of the environment. This algorithm scales well because of the sparse representation of large area environments, it is robust even at large value of sensor noise. This CPP is model free, computationally efficient and consider the robot safety by only considering the admissible locations for navigation. Viewpoints within the unexplored regions of robot workspace are chosen for traversal to cover the environment. The proposed procedure is experimentally verified in a 15 different 3D-gazebo world models using a 2-wheeled differential mobile robot with a Lidar sensor, using ROS framework for implementation. Simulation results shows the effectiveness of this algorithm for the real-world applications.
AB - The ability to plan online paths, that passes through points of interest for coverage of an unknown environment is essential to robot autonomy. Existing state of the art coverage path planning techniques for active exploration (Frontier and Next-Best-View) are occupancy grid maps based, which do not scale well in large area environments, suffer from long term drift and are not robust at large values of sensor range noise, not address robot navigation safety. We proposed a Coverage Path Planning (CPP) algorithm, which is based on a coordinate system based topological map of the admissible locations of the environment. This algorithm scales well because of the sparse representation of large area environments, it is robust even at large value of sensor noise. This CPP is model free, computationally efficient and consider the robot safety by only considering the admissible locations for navigation. Viewpoints within the unexplored regions of robot workspace are chosen for traversal to cover the environment. The proposed procedure is experimentally verified in a 15 different 3D-gazebo world models using a 2-wheeled differential mobile robot with a Lidar sensor, using ROS framework for implementation. Simulation results shows the effectiveness of this algorithm for the real-world applications.
UR - http://www.scopus.com/inward/record.url?scp=85147331643&partnerID=8YFLogxK
U2 - 10.1109/ROBIO55434.2022.10012015
DO - 10.1109/ROBIO55434.2022.10012015
M3 - Conference contribution
AN - SCOPUS:85147331643
T3 - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
SP - 1623
EP - 1628
BT - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2022 through 9 December 2022
ER -