SLS-PLAN-IT: A knowledge-based blackboard scheduling system for Spacelab life sciences missions

The primary scheduling tool in use during the Spacelab Life Science (SLS-1) planning phase was the operations research (OR) based, tabular form Experiment Scheduling System (ESS) developed by NASA Marshall. PLAN-IT is an artificial intelligence based interactive graphic timeline editor for ESS developed by JPL. The PLAN-IT software was enhanced for use in the scheduling of Spacelab experiments to support the SLS missions. The enhanced software SLS-PLAN-IT System was used to support the real-time reactive scheduling task during the SLS-1 mission. SLS-PLAN-IT is a frame-based blackboard scheduling shell which, from scheduling input, creates resource-requiring event duration objects and resource-usage duration objects. The blackboard structure is to keep track of the effects of event duration objects on the resource usage objects. Various scheduling heuristics are coded in procedural form and can be invoked any time at the user's request. The system architecture is described along with what has been learned with the SLS-PLAN-IT project

Threat expert system technology advisor

A prototype expert system was developed to determine the feasibility of using expert system technology to enhance the performance and survivability of helicopter pilots in a combat threat environment while flying NOE (Nap of the Earth) missions. The basis for the concept is the potential of using an Expert System Advisor to reduce the extreme overloading of the pilot who flies NOE mission below treetop level at approximately 40 knots while performing several other functions. The ultimate goal is to develop a Threat Expert System Advisor which provides threat information and advice that are better than even a highly experienced copilot. The results clearly show that the NOE pilot needs all the help in decision aiding and threat situation awareness that he can get. It clearly shows that heuristics are important and that an expert system for combat NOE helicopter missions can be of great help to the pilot in complex threat situations and in making decisions

Intent inferencing with a model-based operator's associate

A portion of the Operator Function Model Expert System (OFMspert) research project is described. OFMspert is an architecture for an intelligent operator's associate or assistant that can aid the human operator of a complex, dynamic system. Intelligent aiding requires both understanding and control. The understanding (i.e., intent inferencing) ability of the operator's associate is discussed. Understanding or intent inferencing requires a model of the human operator; the usefulness of an intelligent aid depends directly on the fidelity and completeness of its underlying model. The model chosen for this research is the operator function model (OFM). The OFM represents operator functions, subfunctions, tasks, and actions as a heterarchic-hierarchic network of finite state automata, where the arcs in the network are system triggering events. The OFM provides the structure for intent inferencing in that operator functions and subfunctions correspond to likely operator goals and plans. A blackboard system similar to that of Human Associative Processor (HASP) is proposed as the implementation of intent inferencing function. This system postulates operator intentions based on current system state and attempts to interpret observed operator actions in light of these hypothesized intentions

Information management in an integrated space telerobot

The in-orbit operations, like space structures inspection, servicing and repairing, is expected to be one of the most significant technological area for application and development of Robotics and Automation in Space Station environment. The Italian National Space Plan (PSN) has started up its strategic programme SPIDER (Space Inspection Device for Extravehicular Repairs), which is scheduled in three phases, with the final goal of performing docking and precision repairing in the Space Station environment. SPIDER system is an autonomous integrated space robot, using mature Artificial Intelligence tools and technics for its operational control. The preliminary results of a study on the information architecture of the spacecraft are described

TDRSS momentum unload planning

A knowledge-based system is described which monitors TDRSS telemetry for problems in the momentum unload procedure. The system displays TDRSS telemetry and commands in real time via X-windows. The system constructs a momentum unload plan which agrees with the preferences of the attitude control specialists and the momentum growth characteristics of the individual spacecraft. During the execution of the plan, the system monitors the progress of the procedure and watches for unexpected problems

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 323)

This bibliography lists 125 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1989. Subject coverage includes; aerospace medicine and psychology, life support systems and controlled environments, safety equipment exobiology and extraterrestrial life, and flight crew behavior and performance

Coordinating complex problem-solving among distributed intelligent agents

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet

Operator assistant to support deep space network link monitor and control

Preparing the Deep Space Network (DSN) stations to support spacecraft missions (referred to as pre-cal, for pre-calibration) is currently an operator and time intensive activity. Operators are responsible for sending and monitoring several hundred operator directivities, messages, and warnings. Operator directives are used to configure and calibrate the various subsystems (antenna, receiver, etc.) necessary to establish a spacecraft link. Messages and warnings are issued by the subsystems upon completion of an operation, changes of status, or an anomalous condition. Some points of pre-cal are logically parallel. Significant time savings could be realized if the existing Link Monitor and Control system (LMC) could support the operator in exploiting the parallelism inherent in pre-cal activities. Currently, operators may work on the individual subsystems in parallel, however, the burden of monitoring these parallel operations resides solely with the operator. Messages, warnings, and directives are all presented as they are received; without being correlated to the event that triggered them. Pre-cal is essentially an overhead activity. During pre-cal, no mission is supported, and no other activity can be performed using the equipment in the link. Therefore, it is highly desirable to reduce pre-cal time as much as possible. One approach to do this, as well as to increase efficiency and reduce errors, is the LMC Operator Assistant (OA). The LMC OA prototype demonstrates an architecture which can be used in concert with the existing LMC to exploit parallelism in pre-cal operations while providing the operators with a true monitoring capability, situational awareness and positive control. This paper presents an overview of the LMC OA architecture and the results from initial prototyping and test activities

A Localized Autonomous Control Algorithm For Robots With Heterogeneous Capabilities In A Multi-Tier Architecture

This dissertation makes two contributions to the use of the Blackboard Architecture for command. The use of boundary nodes for data abstraction is introduced and the use of a solver-based blackboard system with pruning is proposed. It also makes contributions advancing the engineering design process in the area of command system selection for heterogeneous robotic systems. It presents and analyzes data informing decision making between centralized and distributed command systems and also characterizes the efficacy of pruning across different experimental scenarios, demonstrating when it is effective or not. Finally, it demonstrates the operations of the system, raising the technology readiness level (TRL) of the technology towards a level suitable for actual mission use. The context for this work is a multi-tier mission architecture, based on prior work by Fink on a “tier scalable” architecture. This work took a top-down approach where the superior tiers (in terms of scope of visibility) send specific commands to craft in lower tiers. While benefitting from the use of a large centralized processing center, this approach is limited in responding to failures and interference. The work presented herein has involved developing and comparatively characterizing centralized and decentralized (where superior nodes provide information and goals to the lower-level craft, but decisions are made locally) Blackboard Architecture based command systems. Blackboard Architecture advancements (a solver, pruning, boundary nodes) have been made and tested under multiple experimental conditions