PUTN: A Plane-fitting based Uneven Terrain Navigation Framework

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 1 1 Source code: https://github.com/jianzhuozhuTHU/putn. .

Authors

• Zhuozhu Jian
• Zihong Lu
• Xiao Zhou
• Bin Lan
• Anxing Xiao
• Xueqian Wang 0001
• Bin Liang 0001

Keywords

• Point cloud compression
• Training
• Strips
• Uncertainty
• Three-dimensional displays
• Navigation
• Trajectory
• Uneven Terrain
• Urban Planning
• Real Scenarios
• Point Cloud
• Gaussian Process
• Kriging
• Path Planning
• Model Predictive Control
• Random Tree
• Robot Navigation
• Autonomous Navigation
• Nonlinear Model Predictive Control
• Plane Fitting
• Rapidly-exploring Random Tree
• Ground Robots
• Complex Environment
• Sparsity
• Unit Vector
• Linear Interpolation
• Global Plan
• Simultaneous Localization And Mapping
• Unmanned Ground Vehicles
• Local Plane
• Conditional Value At Risk
• Global Path
• Grid Map
• Terrain Analysis
• Terrain Surface