Activity-based mission planning and plan management for autonomous vehicles

W. Hall, J. Farrell
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引用次数: 3

Abstract

This paper presents an activity-based mission planning and plan management framework that has been developed at the Charles Stark Draper Laboratory. The main contributions of this paper are definition of this activity-based implementation and comparison with other planning implementation approaches (e.g., behavior-based); and explanation of how planning, execution, monitoring, and replanning are implemented within this activity-based approach. One of the main benefits of this approach is the ability to separate the mission planning and plan management algorithms from activity specific algorithms and from mission and vehicle specific information. This separation results in an implementation that is highly portable both between missions and vehicles. This framework has been implemented and demonstrated in high-fidelity autonomous land and underwater vehicle simulations, is planned to be implemented on ARPA's UUV in the near future, and is being considered for applications involving other underwater vehicles, autonomous land rovers, and the space shuttle.
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基于活动的自动驾驶车辆任务规划与计划管理
本文介绍了查尔斯·斯塔克·德雷珀实验室开发的基于活动的任务规划和计划管理框架。本文的主要贡献是定义了这种基于活动的实施,并与其他规划实施方法(例如,基于行为的)进行了比较;并解释如何在这种基于活动的方法中实现计划、执行、监视和重新计划。这种方法的主要优点之一是能够将任务规划和计划管理算法与特定活动算法以及任务和飞行器特定信息分离开来。这种分离导致在特派团和车辆之间实现高度可移植性。该框架已在高保真自主陆地和水下航行器模拟中实施和演示,计划在不久的将来在ARPA的UUV上实施,并正在考虑用于涉及其他水下航行器、自主陆地漫游器和航天飞机的应用。
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