Learning robust failure response for autonomous vision based flight
Learning robust failure response for autonomous vision based flight
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DOI：
10.1109/icra.2017.7989684
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
D. Saxena;Vince Kurtz;M. Hebert
中科院分区：
文献类型：
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作者：
D. Saxena;Vince Kurtz;M. Hebert
自主移动机器人对车载系统潜在故障的反应和恢复能力是当前机器人学研究的一个重要领域。随着对强健系统和长期自主性的日益重视，移动机器人必须能够安全和智能地对危险情况做出反应。计算机视觉的最新发展使基于自主视觉的导航成为可能。然而，众所周知，视觉系统并不完美，容易由于可变的照明、地形变化和其他环境变量而失败。我们描述了一个基于经验学习简单故障恢复策略的系统。这包括识别视觉系统何时容易出现故障，并将故障与最有可能帮助机器人恢复的适当反应联系起来。我们在自主四旋翼上实现了这个系统，并证明了用我们的系统学习的行为在从态势感知故障中恢复方面是有效的，从而提高了在杂乱和不确定环境中的可靠性。
The ability of autonomous mobile robots to react to and recover from potential failures of on-board systems is an important area of ongoing robotics research. With increasing emphasis on robust systems and long-term autonomy, mobile robots must be able to respond safely and intelligently to dangerous situations. Recent developments in computer vision have made autonomous vision based navigation possible. However, vision systems are known to be imperfect and prone to failure due to variable lighting, terrain changes, and other environmental variables. We describe a system for learning simple failure recovery maneuvers based on experience. This involves both recognizing when the vision system is prone to failure, and associating failures with appropriate responses that will most likely help the robot recover. We implement this system on an autonomous quadrotor and demonstrate that behaviors learned with our system are effective in recovering from situational perception failure, thereby improving reliability in cluttered and uncertain environments.