Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

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• 作者：Sara Bernardini ; Maria Fox ; Derek Long
• 关键词：Automated task planning ; Autonomy ; UAVs ; Quadcopters ; Search ; and ; tracking ; Monte Carlo methods
• 刊名：Autonomous Robots
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：41
• 期：1
• 页码：181-203
• 全文大小：3929KB
• 刊物类别：Computer Science
• 刊物主题：Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics;
• 出版者：Springer US
• ISSN：1573-7527
• 卷排序：41

文摘

It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the “Parrot AR.Drone2.0” quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions.