Real-time control for manufacturing space shuttle main engines: Work in progress

During the manufacture of space-based assemblies such as Space Shuttle Main Engines, flexibility is required due to the high-cost and low-volume nature of the end products. Various systems have been developed pursuing the goal of adaptive, flexible manufacturing for several space applications, including an Advanced Robotic Welding System for the manufacture of complex components of the Space Shuttle Main Engines. The Advanced Robotic Welding System (AROWS) is an on-going joint effort, funded by NASA, between NASA/Marshall Space Flight Center, and two divisions of Rockwell International: Rocketdyne and the Science Center. AROWS includes two levels of flexible control of both motion and process parameters: Off-line programming using both geometric and weld-process data bases, and real-time control incorporating multiple sensors during weld execution. Both control systems were implemented using conventional hardware and software architectures. The feasibility of enhancing the real-time control system using the problem-solving architecture of Schemer is investigated and described

Abstraction of Agents Executing Online and their Abilities in the Situation Calculus

We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog. We assume that we have both a high-level action theory and a low-level one that represent the agent's behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so

Prospects of a mathematical theory of human behavior in complex man-machine systems tasks

A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued

The astronaut and the banana peel: An EVA retriever scenario

To prepare for the problem of accidents in Space Station activities, the Extravehicular Activity Retriever (EVAR) robot is being constructed, whose purpose is to retrieve astronauts and tools that float free of the Space Station. Advanced Decision Systems is at the beginning of a project to develop research software capable of guiding EVAR through the retrieval process. This involves addressing problems in machine vision, dexterous manipulation, real time construction of programs via speech input, and reactive execution of plans despite the mishaps and unexpected conditions that arise in uncontrolled domains. The problem analysis phase of this work is presented. An EVAR scenario is used to elucidate major domain and technical problems. An overview of the technical approach to prototyping an EVAR system is also presented

SHOP2: An HTN Planning System

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains

A Model of Autonomous System for Scientific Experiments and Spacecraft Control for Deep Space Missions

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, June, 2017The particularities of autonomous control system for deep space missions are described. A new approach for autonomous control system development is proposed and analyzed in details. Some models are analyzed and compared. The general formal model is based on the theory of communicating sequential processes (CSP). Methods for reconfiguration, verification and trace control are described. The software that is appropriate not only for the spacecraft flight path control but also for autonomous control of scientific apparatus operation and science experiments parameters is described. The software enables onboard scientific apparatus to autonomously detect and respond to science events Science algorithms, including onboard event detection, feature detection, change detection, and unusualness detection, are proposed to be used to analyze science data. Thus detecting features of scientific interest these algorithms are used to downlink only significant science data. These onboard science algorithms are inputs to onboard decision-making Replaner that modify the spacecraft observation plan to capture high value science events. This new observation plan is input for the Task execution subsystem of the Autonomous control system (ACS), able to adjust the plan to succeed despite run-time anomalies and uncertainties, and after it is executed by the ACS, which controls onboard scientific apparatus to enable an autonomous goal-directed exploration and data acquisition to maximize science return.Association for the Development of the Information Society, Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Plovdiv University "Paisii Hilendarski

Incremental planning to control a blackboard-based problem solver

To control problem solving activity, a planner must resolve uncertainty about which specific long-term goals (solutions) to pursue and about which sequences of actions will best achieve those goals. A planner is described that abstracts the problem solving state to recognize possible competing and compatible solutions and to roughly predict the importance and expense of developing these solutions. With this information, the planner plans sequences of problem solving activities that most efficiently resolve its uncertainty about which of the possible solutions to work toward. The planner only details actions for the near future because the results of these actions will influence how (and whether) a plan should be pursued. As problem solving proceeds, the planner adds new details to the plan incrementally, and monitors and repairs the plan to insure it achieves its goals whenever possible. Through experiments, researchers illustrate how these new mechanisms significantly improve problem solving decisions and reduce overall computation. They briefly discuss current research directions, including how these mechanisms can improve a problem solver's real-time response and can enhance cooperation in a distributed problem solving network