Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains

被引：11
作者：
Lee, Hojin [1 ]
Kwon, Junsung [1 ]
Kwon, Cheolhyeon [1 ]
机构：
[1] Ulsan Natl Inst Sci & Technol, Dept Mech Engn, Ulsan 44919, South Korea
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2023) | 2023年
基金：
新加坡国家研究基金会;
关键词：
AUTONOMOUS GROUND VEHICLES; OBSTACLE AVOIDANCE; SPEED;
D O I：
10.1109/ICRA48891.2023.10161543
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 safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments. Off-road navigation is subject to uncertain vehicle-terrain interactions caused by different terrain conditions on top of 3D terrain topology. The existing works are limited to adopt overly simplified vehicle-terrain models. The proposed algorithm learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver. Assuring real-time execution, the algorithm is further implemented within parallel computation architecture running on Graphics Processing Units (GPU).
引用
收藏
页码：10061 / 10068
页数：8
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