PUTN: A Plane-fitting based Uneven Terrain Navigation Framework

Zhuozhu Jian1; Zihong Lu2; Xiao Zhou2; Bin Lan1; Anxing Xiao3; Xueqian Wang1; Bin Liang1

10.1109/IROS47612.2022.9981038

2022

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

978-1-6654-7928-8

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

7160-7166

23-27 Oct. 2022

Kyoto, Japan

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT* (PF-RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable "strip". Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community.

Point cloud compression Training Strips Uncertainty Three-dimensional displays Navigation Trajectory

其他

英语

CPCI-S

Joint Funds of the National Natural Science Foundation of China[U1813216]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000909405300016

IEEE

1.Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
2.School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Zhuozhu Jian,Zihong Lu,Xiao Zhou,et al. PUTN: A Plane-fitting based Uneven Terrain Navigation Framework[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:7160-7166.