A Hybrid Procedural/Deductive Executive for Autonomous Spacecraft

The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft. The spacecraft domain places a strong premium on autonomy and requires dynamic recoveries and robust concurrent execution, all in the presence of tight real-time deadlines, changing goals, scarce resource constraints, and a wide variety of possible failures. To achieve this level of execution robustness, we have integrated a procedural executive based on generic procedures with a deductive model-based executive. A procedural executive provides sophisticated control constructs such as loops, parallel activity, locks, and synchronization which are used for robust schedule execution, hierarchical task decomposition, and routine configuration management. A deductive executive provides algorithms for sophisticated state inference and optimal failure recovery planning. The integrated executive enables designers to code knowledge via a combination of procedures and declarative models, yielding a rich modeling capability suitable to the challenges of real spacecraft control. The interface between the two executives ensures both that recovery sequences are smoothly merged into high-level schedule execution and that a high degree of reactivity is retained to effectively handle additional failures during recovery.     

The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control

This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-the-shelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate sensor information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.     

Structured Reactive Controllers

Service robots, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. This requires them to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. In this paper, we show how structured reactive controllers (SRCs) satisfy these requirements. The novel feature of SRCs is that they employ and reason about plans that specify and synchronize concurrent percept-driven behavior. Powerful control abstractions enable SRCs to integrate physical action, perception, planning, and communication in a uniform framework and to apply fast but imperfect computational methods without sacrificing reliability and flexibility. Concurrent plans are represented in a transparent and modular form so that automatic planning processes can reason about the plans and revise them. We present experiments in which SRCs are used to control two autonomous mobile robots. In one of them an SRC controlled the course of action of a museum tourguide robot that has operated for thirteen days, more than ninetyfour hours, completed 620 tours, and presented 2668 exhibits.     

航天器威胁规避智能自主控制技术研究综述

当前, 轨道空间日益拥挤、太空竞争不断加剧, 对航天器执行既定任务时的轨道威胁自主应对能力提出了新的挑战, 使得航天器智能自主控制技术迎来新的发展机遇. 在调研分析了轨道威胁感知、自主决策规划、规避机动动作执行、自主控制系统架构相关研究进展的基础上, 总结提出了威胁规避智能自主控制面临的主要瓶颈问题, 并分析指出发展“感知−决策−执行”一体化控制是破解瓶颈难题的有效手段, 最后从一体化控制系统建模、设计、分析与验证多方面, 系统讨论了威胁规避智能自主控制需要重点关注的若干基础问题, 为未来航天器智能自主控制的理论研究和技术发展提供启发和参考.     

卫星自主与协同任务调度规划综述

如何利用有限的星上资源执行复杂繁重的空间任务长期以来备受关注,随着卫星载荷能力不断提升,响应时间要求不断缩短,卫星自主与协同任务调度规划研究的重要性逐渐增大.本文从单星任务调度规划为起点,对单星地面离线任务调度规划、单星星上在线任务调度规划、单星星地一体化自主任务调度规划研究现状进行梳理与分析,然后延展至多星领域,综述了多星地面离线协同任务调度规划、多星星上在线协同任务调度规划、多星星地一体协同任务调度规划研究现状.最后,对卫星自主与协同任务调度规划研究进行了总结与展望.