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The Operations Integration Plan is presented, which is to provide functional definition of the activities necessary to develop and integrate shuttle operating plans and facilities to support flight, flight control, and operations. It identifies the major tasks, the organizations responsible, their interrelationships, the sequence of activities and interfaces, and the resultant products related to operations integration.
The primary objectives of the Payload Crew Activity Planning Integration task were to: (1) Determine feasible, cost-effective payload crew activity planning integration methods. (2) Develop an implementation plan and guidelines for payload crew activity plan (CAP) integration between the JSC Orbiter planners and the Payload Centers. Subtask objectives and study activities were defined as: (1) Determine Crew Activity Planning Interfaces. (2) Determine Crew Activity Plan Type and Content. (3) Evaluate Automated Scheduling Tools. (4) Develop a draft Implementation Plan for Crew Activity Planning Integration. The basic guidelines were to develop a plan applicable to the Shuttle operations timeframe, utilize existing center resources and expertise as much as possible, and minimize unnecessary data exchange not directly productive in the development of the end-product timelines.
Integrated planning and execution of teleoperations in space with time delays is shown. The topics include: 1) The Problem; 2) Future Robot Surgery? 3) Approach Overview; 4) Robonaut; 5) Normal Planning and Execution; 6) Planner Context; 7) Implementation; 8) Use of JSHOP2; 9) Monitoring and Testing GUI; 10) Normal sequence: first the supervisor acts; 11) then the robot; 12) Robot might be late; 13) Supervisor can work ahead; 14) Deviations from Plan; 15) Robot State Change Example; 16) Accomplished goals skipped in replan; 17) Planning continuity; 18) Supervisor Deviation From Plan; 19) Intentional Deviation; and 20) Infeasible states.
This paper describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of changing operating context.
Electronic systems integration plan for radio astronomy Explorer satellite
The Entropy Reduction Engine architecture for integrating planning, scheduling, and control is examined. The architecture is motivated through a NASA mission scenario and a brief list of design goals. An overview is presented of the Entropy Reduction Engine architecture by describing its major components, their interactions, and the way in which these interacting components satisfy the design goals.
The Heuristic Scheduling Testbed System (HSTS), a software architecture for integrated planning and scheduling, is discussed. The architecture has been applied to the problem of generating observation schedules for the Hubble Space Telescope. This problem is representative of the class of problems that can be addressed: their complexity lies in the interaction of resource allocation and auxiliary task expansion. The architecture deals with this interaction by viewing planning and scheduling as two complementary aspects of the more general process of constructing behaviors of a dynamical system. The principal components of the software architecture are described, indicating how to model the structure and dynamics of a system, how to represent schedules at multiple levels of abstraction in the temporal database, and how the problem solving machinery operates. A scheduler for the detailed management of Hubble Space Telescope operations that has been developed within HSTS is described. Experimental performance results are given that indicate the utility and practicality of the approach.
The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.
Several current NASA programs such as the EOSDIS Core System (ECS) have data processing and data management requirements that call for an integrated planning and scheduling capability. In this paper, we describe the experience of applying advanced scheduling technology operationally, in terms of what was accomplished, lessons learned, and what remains to be done in order to achieve similar successes in ECS and other programs. We discuss the importance and benefits of advanced scheduling tools, and our progress toward realizing them, through examples and illustrations based on ECS requirements. The first part of the paper focuses on the Data Archive and Distribution (DADS) V0 Scheduler. We then discuss system integration issues ranging from communication with the scheduler to the monitoring of system events and re-scheduling in response to them. The challenge of adapting the scheduler to domain-specific features and scheduling policies is also considered. Extrapolation to the ECS domain raises issues of integrating scheduling with a product-generation planner (such as PlaSTiC), and implementing conditional planning in an operational system. We conclude by briefly noting ongoing technology development and deployment projects being undertaken by HTC and the ISTB.
NASA defines Human Systems Integration (HSI) as part of the overall systems engineering and acquisition strategy for space systems. The HSI Plan defines how HSI activities will be implemented across the lifecycle of the mission, as required by NPR 7123.1C, NASA Systems Engineering Processes and Requirements, and NPR 8705.2C Human-Rating Requirements for Space Systems. The goal of this presentation is to share with government and industry how an HSI Plan can be implemented. The presentation will cover HSI implementation for flight systems, vehicle processing, and interfaces. These are divided into six NASA HSI Domains: human factors engineering, operations resources, safety, training, maintainability and supportability, habitability and environment. HSI activities go across the mission’s lifecycle from pre-formulation and acquisition through design, development, operations, maintenance, and decommissioning. The HSI Plan includes a description of the HSI activities and products that are essential for human rating, operability, maintainability, supportability, and affordability of the mission systems. It also describes the role of the HSI Team required as part of the Human Rating process. The HSI Plan utilizes the operational expertise within NASA to ensure designs and testing are successful, leading to acceptable human spaceflight vehicles.
The National Aeronautics and Space Administration (NASA) and the aviation industry have recognized a need for developing a method to identify and combine resources to carry out research and testing more efficiently. The Integrated Vehicle Health Management (IVHM) Research Test and Integration Plan (RTIP) Wiki is a tool that is used to visualize, plan, and accomplish collaborative research and testing. Synergistic test opportunities are developed using the RTIP Wiki, and include potential common resource testing that combines assets and personnel from NASA, industry, academia, and other government agencies. A research scenario is linked to the appropriate IVHM milestones and resources detailed in the wiki, reviewed by the research team members, and integrated into a collaborative test strategy. The scenario is then implemented by creating a test plan when appropriate and the research is performed. The benefits of performing collaborative research and testing are achieving higher Technology Readiness Level (TRL) test opportunities with little or no additional cost, improved quality of research, and increased communication among researchers. In addition to a description of the method of creating these joint research scenarios, examples of the successful development and implementation of cooperative research using the IVHM RTIP Wiki are given.
The Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control, is described. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, temporal projection, and situated control rule execution. Experience with this architecture has motivated the recent integration of learning. The learning methods are described along with their impact on architecture performance.
Our research is developing persistent agents that can achieve complex tasks in dynamic and uncertain environments. We refer to such agents as taskable, reactive agents. An agent of this type requires a number of capabilities. The ability to execute complex tasks necessitates the use of strategic plans for accomplishing tasks; hence, the agent must be able to synthesize new plans at run time. The dynamic nature of the environment requires that the agent be able to deal with unpredictable changes in its world. As such, agents must be able to react to unanticipated events by taking appropriate actions in a timely manner, while continuing activities that support current goals. The unpredictability of the world could lead to failure of plans generated for individual tasks. Agents must have the ability to recover from failures by adapting their activities to the new situation, or replanning if the world changes sufficiently. Finally, the agent should be able to perform in the face of uncertainty. The Cypress system, described here, provides a framework for creating taskable, reactive agents. Several features distinguish our approach: (1) the generation and execution of complex plans with parallel actions; (2) the integration of goal-driven and event driven activities during execution; (3) the use of evidential reasoning for dealing with uncertainty; and (4) the use of replanning to handle run-time execution problems. Our model for a taskable, reactive agent has two main intelligent components, an executor and a planner. The two components share a library of possible actions that the system can take. The library encompasses a full range of action representations, including plans, planning operators, and executable procedures such as predefined standard operating procedures (SOP's). These three classes of actions span multiple levels of abstraction.
Planning will be an essential part of future autonomous robots and integrated intelligent systems. This paper focuses on learning problem solving knowledge in planning systems. The system is based on a common representation for macros, abstractions, and cases. Therefore, it is able to exploit both classical and case based techniques. The general operators in a successful plan derivation would be assessed for their potential usefulness, and some stored. The feasibility of this approach was studied through the implementation of a learning system for abstraction. New macros are motivated by trying to improve the operatorset. One heuristic used to improve the operator set is generating operators with more general preconditions than existing ones. This heuristic leads naturally to abstraction hierarchies. This investigation showed promising results on the towers of Hanoi problem. The paper concludes by describing methods for learning other problem solving knowledge. This knowledge can be represented by allowing operators at different levels of abstraction in a refinement.
Planetary rovers exploring the surface of Mars face a challenging operational environment that requires close cooperation between deliberative planning and behavioral execution in order to most efficiently leverage the robot’s capabilities into science value returned to earth. The Self-Reliant Rovers project envisions future rover missions that require only occasional high-level direction from human controllers to successfully conduct detailed in-situ studies of its Martian environs. To achieve this high degree of autonomy, this work leverages a spectrum of planning and execution techniques that allow the rover to respond appropriately to both opportunity and adversity it encounters. Small perturbations are accommodated at first by behavioral adaptation, with more and more extensive disruptions handled in turn by executive administration of plan flexibility, heuristic-guided plan repair strategies, and finally comprehensive replanning from science campaign goals. The integrated system has been deployed and tested on a terrestrial rover in an environment and under scenarios that anticipate those faced by future Mars rovers. This paper recounts complexities of planning and execution coordination faced in the rover domain and the practical solutions employed to address them. Particular emphasis is given to lessons from the field and foibles ripe for remedy by future advances in planning and execution research.
Over a period of several years, the software systems that plan and schedule the use of NASA’s Deep Space Network (DSN) for the projects it serves have been upgraded from a disparate set of decades-old software components, to an integrated suite covering long-range planning and forecasting, all the way to real-time scheduling. The most recent component of this suite is known as LAPS, for Loading Analysis and Planning Software, and is responsible for long-term planning and forecasting, including studies and analysis of new missions, changed mission requirements, downtime, and new or changed antenna capabilities. This paper discusses the architecture of LAPS and its interfaces with other elements of DSN planning and scheduling, its user interfaces, and some lessons learned from development and deployment.
To achieve the goal of building an autonomous agent, the usually disjoint capabilities of planning, execution, and learning must be used together. An architecture, called MAX, within which cognitive capabilities can be purposefully and intelligently integrated is described. The architecture supports the codification of capabilities as explicit knowledge that can be reasoned about. In addition, specific problem solving, learning, and integration knowledge is developed.
We describe a prototype Vehicle System Manager (VSM) for NASA’s Gateway, a human-capable spacecraft that will also be capable of autonomous operations. The VSM consists of an execution system, planner, and fault management system, integrated via an over-arching mission management compo- nent. We describe the VSM architecture and each of its com- ponents. We describe a series of use cases, centered on a spacecraft propulsive operation that can fail at different times, for different reasons, and how the VSM detects and responds to these failures. We show the VSM is capable of detecting faults and loss of capability, and subsequently replanning, in the presence of each failure scenario.