Learning for Autonomous Navigation Advances in Machine Learning for Rough Terrain Mobility
Learning for Autonomous Navigation Advances in Machine Learning for Rough Terrain Mobility
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DOI：
10.1109/mra.2010.936946
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发表时间：
2010-06-01
影响因子：
5.7
通讯作者：
Stentz, Anthony
中科院分区：
文献类型：
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作者：
Bagnell, James Andrew;Bradley, David;Stentz, Anthony
移动的机器人在L自然非结构化地形中的自主导航是野外机器人技术的主要挑战之一。近年来，在野外机器人领域，自主导航技术取得了巨大的进步。机器学习在这些进步中发挥了越来越重要的作用。美国国防部高级研究计划局（DARPA）的UGCV-感知器集成（UPI）计划的设想是采取一种新的方法来自主户外移动的机器人设计的各个方面，从车辆设计到感知和控制系统的设计，目标是实现性能的飞跃，使下一代机器人应用于商业，工业和军事应用。UPI计划解决的基本问题是，在给定的一系列路点的情况下，机器人能够在尽可能短的时间内安全地自主穿越A点到B点，这些路点在复杂的非结构化地形中相隔0.2-2 km。为了实现这一目标，机器学习技术被大量用于提供强大和自适应的性能，同时减少所需的开发和部署时间。本文介绍了自主系统，破碎机，开发的UPI计划和学习方法，帮助其成功的表现。
Autonomous navigation by a mobile robot through L natural, unstructured terrain is one of the premier k challenges in field robotics. Tremendous advances V in autonomous navigation have been made recently in field robotics. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, from vehicle design to the design of perception and control systems with the goal of achieving a leap in performance to enable the next generation of robotic applications in commercial, industrial, and military applications. The essential problem addressed by the UPI program is to enable safe autonomous traverse of a robot from Point A to Point B in the least time possible given a series of waypoints in complex, unstructured terrain separated by 0.2-2 km. To accomplish this goal, machine learning techniques were heavily used to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This article describes the autonomous system, Crusher, developed for the UPI program and the learning approaches that aided in its successful performance.