A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

被引：0
|
作者：
Rastgoftar, Hossein [1 ]
Zhang, Bingxin [2 ]
Atkins, Ella M. [1 ]
机构：
[1] Univ Michigan, Aerosp Engn Dept, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Elect & Comp Engn Dept, Ann Arbor, MI 48109 USA
来源：
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) | 2018年
关键词：
VEHICLES;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel data-driven approach for vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desired trajectory between waypoints such that infeasible control states and collisions with obstacles are avoided. The LPP also updates the GRP with real-time sensing and control data. A low-level feedback controller applies feedback linearization to asymptotically track the specified LPP trajectory. Autonomous driving simulation results are presented for traversal of terrains in Oregon and Indiana case studies.
引用
收藏
页码：5876 / 5883
页数：8
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