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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.
Currently, mission planning for the International Space Station (ISS) is largely affected by ground operators in mission control. The task of creating a week-long mission plan for ISS crew takes dozens of people multiple days to complete, and is often created far in advance of its execution. As such, re-planning or adapting to changing real-time constraints or emergent issues is similarly taxing. As we design for future mission operations concepts to other planets or areas with limited connectivity to Earth, more of these ground-based tasks will need to be handled autonomously by the crew onboard.There is a need for a highly usable (including low training time) tool that enables efficient self-scheduling and execution within a single package. The ISS Program has identified Playbook as a potential option. It already has high crew acceptance as a plan viewer from previous analogs and can now support a crew self-scheduling assessment on ISS or on another mission. The goals of this work, a collaboration between the Human Research Program and the ISS Program, are to inform the design of systems for more autonomous crew operations and provide a platform for research on crew autonomy for future deep space missions. Our second year of the research effort have included new insights on the crew self-scheduling sessions performed by the crew through use on the HERA (Human Exploration Research Analog) and NEEMO (NASA Extreme Environment Mission Operations) analogs. Use on the NEEMO analog involved two self-scheduling strategies where the crew planned and executed two days of EVAs (Extra-Vehicular Activities). On HERA year two represented the first HERA campaign where we were able to perform research tasks. This involved selected flexible activities that the crew could schedule, mock timelines where the crew completed more complex planning exercises, usability evaluation of the crew self-scheduling features, and more insights into the limit of plan complexity that the crew could effectively self-schedule. In parallel we have added in new features and functionality in the Playbook tool based off of our insights from crew self-scheduling in the NASA analogs. In particular this year we have added in the ability for the crew to add, edit, and remove their own activities in the Playbook tool, expanding the type of planning and re-planning possible in the tool and opening up the ability for more free form plan creation. The ability to group and manipulate groups of activities from the plan task list was also added, allowing crew members to add predefined sets of activities onto their mission timeline. In addition we also added a way for crew members to roll back changes in their plan, in order to allow an undo like capability. These features expand and complement the initial self-scheduling features added in year one with the goal of making crew autonomous planning more efficient. As part of this work we have also finished developing the first version of our Playbook Data Analysis Tool, a research tool built to interpret and analyze the unobtrusively collected data obtained during the NASA analog missions through Playbook. This data which includes user click interaction as well as plan change information, through the Playbook Data Analysis Tool, allows us to playback this information as if a video camera was mounted over the crewmember's tablet. While the primary purpose of this tool is to allow usability analysis of crew self-scheduling sessions used on the NASA analog, since the data collected is structured, the tool can automatically derive metrics that would be traditionally tedious to achieve without manual analysis of video playback. We will demonstrate and discuss the ability for future derived metrics to be added to the tool. In addition to the current data and results gathered in year two we will also discuss the preparation and goals of our International Space Station (ISS) onboard technology demonstration with Playbook. This technology demonstration will be preformed as part of the CAST payload starting in late 2016.
This paper describes how continuous planning and execution techniques can be used to perform intelligent decision-making for an autonomous Mars rover.
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.
The National Transmission Planning Study (NTP Study) was led by the U.S. Department of Energy's Grid Deployment Office, in partnership with the National Renewable Energy Laboratory and Pacific Northwest National Laboratory. The study sought to develop new national grid-scale planning tools and methods that can be used by industry, especially when planning for interregional transmission capacity needs; identify potential transmission solutions that will provide broad-scale benefits to electric customers under a wide range of potential futures; inform planning processes for regional and interregional transmission; and identify interregional and national strategies to maintain grid reliability as the grid transitions, including to a reliance on low- and zero-carbon energy resources. The NTP Study is presented as a collection of six chapters.
In this paper we present MEXEC - a lightweight on-board planning and execution system that monitors spacecraft state to robustly respond to current conditions. In addition it projects the remaining plan forward in time to detect conflicts and revise the plan. Most current planetary missions use sequence based commanding from the ground,which limits the ability to respond to on-board state varying from planned for state. This results in planning for worst-case execution time, power utilization, data volume and other resources and leads to under utilization of the spacecraft capability. It also limits the ability to respond to faults. Due to the high radiation environment at Jupiter the probability of flight software resets is high. Therefore, of particular interest to the planned Europa Mission is the capability to restart the science plan after flight software resets. In this paper we present results from running Europa flyby scenarios with flight software resets in the Europa flight software test environment. Preliminary results with the prototype scenario show MEXEC takes less than a tenth of a second to respond to resets. The development was done as part of Europa-focused flight software, but MEXEC was designed to be applicable to landers and rovers as well.
Simulation and visualization of rover behavior are critical capabilities for scientists and rover operators to construct, test, and validate plans for commanding a remote rover. The VIPER system links these capabilities. using a high-fidelity virtual-reality (VR) environment. a kinematically accurate simulator, and a flexible plan executive to allow users to simulate and visualize possible execution outcomes of a plan under development. This work is part of a larger vision of a science-centered rover control environment, where a scientist may inspect and explore the environment via VR tools, specify science goals, and visualize the expected and actual behavior of the remote rover. The VIPER system is constructed from three generic systems, linked together via a minimal amount of customization into the integrated system. The complete system points out the power of combining plan execution, simulation, and visualization for envisioning rover behavior; it also demonstrates the utility of developing generic technologies. which can be combined in novel and useful ways.
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The Aerial Onboard Autonomous Science Investigation System (AerOASIS) system provides autonomous planning and execution capabilities for aerial vehicles (see figure). The system is capable of generating high-quality operations plans that integrate observation requests from ground planning teams, as well as opportunistic science events detected onboard the vehicle while respecting mission and resource constraints. AerOASIS allows an airborne planetary exploration vehicle to summarize and prioritize the most scientifically relevant data; identify and select high-value science sites for additional investigation; and dynamically plan, schedule, and monitor the various science activities being performed, even during extended communications blackout periods with Earth.
GOES-16 was launched in 2016 using GPS at GEO, a first for civil space. With the subsequent launch of GOES-17 in 2018, followed by GOES-18 in 2022, we have accumulated over a decade of error free GPS navigation experience at GEO. Confident in GPS performance at GEO, the next generation/NASA geosynchronous weather satellite program GeoXO will require the spacecraft flight software to automate station keeping and momentum management maneuver planning and execution. Coupled with low thrust propulsion, it gives us assurance that on-board maneuver planning and execution can be implemented at a very low risk, allowing instruments to operate through maneuvers while maintaining a more accurate orbital slot and reducing operational costs. In this paper, we discuss how GOES-R maneuver planning is currently performed on the ground and contrast this with our vision of how it might be automated on-board.
Not only has the number of launched spacecraft per year exploded over the past few years, but also spacecraft are getting progressively more complex, as flyby missions give way to remote orbiters, which in turn give way to rovers and other in situ explorers. To address the software issues in this expanding mission set, JPL started the Mission Data System (MDS) project, an effort to make flight software engineering more straightforward and less prone to error through the eplicit modeling of spacecraft state. This paper presents how MDS performs mission planning and execution in the context of explicitly managing spacecraft state.
The subject paper shows promise in leading to a useful system for integrating deliberative planning, plan repair, and execution control in a dynamic environment with real-time constraints. The conditions in which the system described would enable such integration seem to be ones that would apply to significant practical problems for an autonomous rover.
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The Task Control Architecture (TCA) provides communication and coordination facilities to construct distributed, concurrent robotic systems. The use of TCA in a system that walks a legged robot through rugged terrain is described. The walking system, as originally implemented, had a sequential sense-plan-act control cycle. Utilizing TCA features for task sequencing and monitoring, the system was modified to concurrently plan and execute steps. Walking speed improved by over 30 percent, with only a relatively modest conversion effort.
Writing autonomous software is complex, requiring the coordination of functionally and technologically diverse software modules. System and mission engineers must rely on specialists familiar with the different software modules to translate requirements into application software. Also, each module often encodes the same requirement in different forms. The results are high costs and reduced reliability due to the difficulty of tracking discrepancies in these encodings. In this paper we describe a unified approach to planning and execution that we believe provides a unified representational and computational framework for an autonomous agent. We identify the four main components whose interplay provides the basis for the agent's autonomous behavior: the domain model, the plan database, the plan running module, and the planner modules. This representational and problem solving approach can be applied at all levels of the architecture of a complex agent, such as Remote Agent. In the rest of the paper we briefly describe the Remote Agent architecture. The new agent architecture proposed here aims at achieving the full Remote Agent functionality. We then give the fundamental ideas behind the new agent architecture and point out some implication of the structure of the architecture, mainly in the area of reactivity and interaction between reactive and deliberative decision making. We conclude with related work and current status.
A project undertaken for the European Space Agency (ESA) is presented. The project is developing a knowledge based software system for planning and scheduling of activities for spacecraft assembly, integration, and verification (AIV). The system extends into the monitoring of plan execution and the plan repair phase. The objectives are to develop an operational kernel of a planning, scheduling, and plan repair tool, called OPTIMUM-AIV, and to provide facilities which will allow individual projects to customize the kernel to suit its specific needs. The kernel shall consist of a set of software functionalities for assistance in initial specification of the AIV plan, in verification and generation of valid plans and schedules for the AIV activities, and in interactive monitoring and execution problem recovery for the detailed AIV plans. Embedded in OPTIMUM-AIV are external interfaces which allow integration with alternative scheduling systems and project databases. The current status of the OPTIMUM-AIV project, as of Jan. 1991, is that a further analysis of the AIV domain has taken place through interviews with satellite AIV experts, a software requirement document (SRD) for the full operational tool was approved, and an architectural design document (ADD) for the kernel excluding external interfaces is ready for review.
The Intelligent Distributed Execution Architecture (IDEA) is a real-time architecture that exploits artificial intelligence planning as the core reasoning engine for interacting autonomous agents. Rather than enforcing separate deliberation and execution layers, IDEA unifies them under a single planning technology. Deliberative and reactive planners reason about and act according to a single representation of the past, present and future domain state. The domain state behaves the rules dictated by a declarative model of the subsystem to be controlled, internal processes of the IDEA controller, and interactions with other agents. We present IDEA concepts - modeling, the IDEA core architecture, the unification of deliberation and reaction under planning - and illustrate its use in a simple example. Finally, we present several real-world applications of IDEA, and compare IDEA to other high-level control approaches.
The traditional form of spacecraft commanding is with sequences that specify when commands should execute based on a schedule generated on the ground. Some sequences have control logic and event driven responses to increase flexibility, but it is limited. An approach to increase autonomy is to use goal-based planning and commanding. Using this paradigm, intention and behavior is modeled on board the spacecraft. In this paper we describe MEXEC (Multi-mission EXECutive), a multi-mission, task-based, onboard planning and execution software designed specifically to be used as flight software. As a path to infusion for future flight projects, we describe two experiments performed on the ASTERIA CubeSat and testbed that demonstrate that MEXEC can be integrated and used for spacecraft operations and increase robustness and science return compared to the standard sequences that were being used.