Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain

作     者：Datar, Aniket Pan, Chenhui Xiao, Xuesu 

作者机构：George Mason Univ Dept Comp Sci Fairfax VA 22030 USA 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2025年第10卷第2期

页      面：1505-1512页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Science Foundation (NSF) Army Research Office (ARO) [W911NF2220242, W911NF2320004, W911NF2420027] US Air Forces Central (AFCENT) Google DeepMind (GDM) Clearpath Robotics Raytheon Technologies 

主　　题：Robots Mobile robots Navigation Vehicle dynamics Trajectory Solid modeling Three-dimensional displays Space vehicles Planning Collision avoidance Autonomous vehicle navigation motion and path planning model learning for control 

摘      要：Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this letter, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.