HelioSwarm: The Nature of Turbulence in Space Plasmas is a transformational mission to explore the dynamic three-dimensional mechanisms controlling the physics of plasma turbulence, a ubiquitous process occurring in the heliosphere and plasmas throughout the universe. Turbulence is the process by which energy contained in fluctuating magnetic fields and plasma motion cascades from large to smaller spatial scales. HelioSwarm achieves its science goals by making simultaneous measurements across a wide range of measurement baselines, spanning magnetohydrodynamic scales (1000's of km) to sub-ion heating scales (10's of km), using a novel nine-spacecraft swarm. The swarm operates in a high-altitude lunar resonant Earth orbit (2 week period, +/- 63 R-E apogee, +/- 13 R-E perigee), giving it access to both the pristine solar wind and regions of strongly driven turbulence (specifically the magnetosphere and foreshock), and utilizes customized relative orbital motion of the swarm members to produce the range of measurement baselines and configurations. The swarm comprises eight "node" spacecraft, manufactured by Blue Canyon Technologies, and a "hub" spacecraft produced by Northrop Grumman Corp. The hub serves as a communications relay, with all communications between the ground and the nodes flowing through it. Mission operations are conducted within the MultiMission Operations Center at the NASA Ames Research Center and science operations at the University of New Hampshire, Durham. HelioSwarm was selected in 2022 as one of NASA's newest Heliophysics Explorer missions to proceed from mission concept into mission implementation, with a target launch in 2029. This paper provides an overview of the mission's science goals and objectives, the mission design, and the concept of operations, with an emphasis on how the swarm aspects of the mission both enable the science measurements and present unique operational challenges. The paper then describes the proposed development approach for the mission operations system and ground data system which relies on a selective combination of scaling strategies to meet the challenges.

Future long-duration missions will require astronauts to act more autonomously, manage their schedules, and replan timelines as anomalies and discoveries occur. Astronauts are not professional planners, however, and the complexity of schedules that novice planners can complete successfully is not fully understood. To identify the primary factors which contribute to scheduling task complexity, we conducted a human-in-the-loop study and developed planning algorithms to investigate how the type and amount of constraints affect the difficulty of scheduling and rescheduling. We created rankings of difficulty using a combination of human performance metrics from experimental planning tasks and metrics describing the final plans that participants scheduled. Using the results of our scheduling and rescheduling algorithm algorithms, we created a similar ranking with which to compare. We created rankings which compared well between the experimental and algorithm results for the scheduling task, but the rescheduling task proved more difficult to estimate.

We present an approach to planning rover traverses in a domain that includes temporal-spatial constraints. We are using the NASA Resource Prospector mission as a reference mission in our research. The primary objective of this mission is to assess the feasibility of in-situ resource utilization (ISRU) on the lunar surface. One of the mission operations constraints is that the rover is generally required to avoid being in shadow, because it is solar-powered. This requirement depends on where the rover is located and when it is at that location. Such a temporal-spatial constraint makes traverse planning more challenging for both humans and machines. We present a mixed-initiative traverse planner which helps address this challenge. This traverse planner is part of the Exploration Ground Data Systems (xGDS), which we have enhanced with new visualization features, new analysis tools, and new automation for path planning, in order to be applicable to the Resource Prospector mission. The key concept that is the basis of the analysis tools and that supports the automated path planning is reachability in this dynamic environment due to the temporal-spatial constraints.

In this paper, we describe Jigsaw: a suite of AI software tools for deep-space nano-satellite (“NanoSat”) communication scheduling. Jigsaw is intended to support the 13 NanoSat missions set to launch on NASA’s Space Launch System (SLS) / Orion Exploration Mission 1 (EM1) in 2019. Rather than have each of these missions independently submit requests to the Deep Space Network (DSN) process and negotiate with all other competing missions, Jigsaw can be used to generate a joint request that has already eliminated conflict among the 13 missions. This can greatly reduce the cost of scheduling and negotiating for each mission. This is especially useful during the first week of the EM-1 mission when all the spacecraft have critical events and are all competing for the same DSN assets. In order to automate this deconfliction process, the communication pass requests from the individual missions include both the minimal requirements and the ideal preferences, and each pass is assigned a priority. The constraints and preferences specified in the requests are used to guide the generation of a joint schedule that satisfies all constraints, maximizes the achievement of the preferences, and is fair across the missions. We describe the specification of the communication requests, which contain both hard constraints and preferences, and then we describe our current approach to solving this scheduling problem. The approach is exemplified on a realistic example involving three of the EM-1 NanoSat missions.

The Mojave Volatiles Prospector (MVP) project is a science-driven field program with the goal of producing critical knowledge for conducting robotic exploration of the Moon. Specifically, MVP focuses on studying a lunar mission analog to characterize the form and distribution of lunar volatiles. Although lunar volatiles are known to be present near the poles of the Moon, the three dimensional distribution and physical characteristics of lunar polar volatiles are largely unknown. A landed mission with the ability to traverse the lunar surface is thus required to characterize the spatial distribution of lunar polar volatiles. NASA's Resource Prospector (RP) mission is a lunar polar rover mission that will operate primarily in sunlit regions near a lunar pole with near-real time operations to characterize the vertical and horizontal distribution of volatiles. The MVP project was conducted as a field campaign relevant to the RP lunar mission to provide science, payload, and operational lessons learned to the development of a real-time, short-duration lunar polar volatiles prospecting mission. To achieve these goals, the MVP project conducted a simulated lunar rover mission to investigate the composition and distribution of surface and subsurface volatiles in a natural environment with an unknown volatile distribution within the Mojave Desert, improving our understanding of how to find, characterize, and access volatiles on the Moon. (C) 2016 Published by Elsevier Ltd on behalf of COSPAR.

NASA's Lunar Atmosphere and Dust Environment Explorer (LADEE) mission was both a lunar science and a technology demonstration mission. The goals identified for LADEE were to determine the composition of the lunar atmosphere and investigate the processes that control its distribution and dynamics, and to determine whether dust is present in the lunar exosphere and reveal the processes that contribute to its sources and variability. LADEE was also developed to serve as a platform for the Lunar Laser Communications Demonstration (LLCD), which had the goal of demonstrating the viability of high-speed optical communication to and from the Moon. LADEE met all of these objectives by operating a robotic spacecraft in a low-altitude, near circular, near equatorial lunar orbit where remote sensing and in-situ instruments measured the Moon's atmosphere and dust environment, and the LLCD demonstrated optical communications at lunar distances. The spacecraft was launched in September of 2013, and spent approximately one month in a transfer orbit before being inserted into lunar orbit in October. It orbited the moon for 188 days, logging time over five lunar synodic months, i.e., “lunations”, before being decommissioned via surface impact in April of 2014. LADEE exceeded its baseline mission duration by greater than 40% in terms of time in the science orbit, and greater than 200% in terms of science data return. This paper summarizes the LADEE mission architecture and describes the operational phase of the LADEE mission in detail. The combination of an aggressive science campaign, the demonstration of a new optical communications payload, and the first-use of a new low-cost spacecraft bus resulted in an operational phase filled with challenges, both planned and unplanned. We explain our approach to orbit determination, maneuver planning, attitude planning, activity planning and command sequencing, which yielded exceedingly positive results in the face of a demanding operational timeline consisting of hundreds of interleaved instrument and spacecraft activities. In addition, we discuss the team's identification of, and response to, several in-flight anomalies including a shutdown of the spacecraft's reaction wheels immediately following launch and an on-going unexpected behavior of the on-board star-tracker and attitude state estimation system. Finally, we reflect on the operations experience overall, the successes that LADEE enjoyed, and some suggestions for future lunar missions.

This paper describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric Dust Environment Explorer). We present the approach taken to reduce the complexity of the activity planning task in order to effectively perform it within the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft’s orbit, given their dynamic nature due to the continually updated orbit determination solution.

Systematic temporally flexible solving has proved useful in scheduling with temporal and resource constraints. However, determining resource flaws in a flexible framework is a complex task even for activities with instantaneous resource impacts, and becomes more daunting for linear or more complex impacts. Plan stability is also important for many applications; as new constraints arise, the changes to an existing plan to accommodate them should be minimized to a degree consistent with rapid solving. In this paper we present a method that uses an evolving grounded solution to guide a flexible resource solving process. To promote stability, the successive grounded solutions satisfy a minimal perturbation criterion. We also discuss an encoding of state constraints as numeric resource transactions and some theoretical implications for state reasoning.

A mixed-initiative approach to activity planning for space mission operations was introduced in the Mars Exploration Rover mission, and has been extended and adapted to other missions. The approach involves a collaboration between a human planner and automated tools that reason about activities and constraints. One important class of constraints arises from state requirements and effects. The mixed-initiative framework passively detects and reports constraint violations. At the user's request, it can also offer suggestions, obtained through automated planning techniques, for actively fixing certain violations. Due to the need for a rapid response, active solving previously used a timeline insertion strategy that limited the types of violations that could be fixed, whereas the passive checking employed an encoding of the state constraints as resource constraints that identified all the violations. In this paper, we report on an extension of the active solver to handle resource problems, allowing a unification of the passive and active strategies.

The LCROSS (Lunar Crater Observation and Sensing Satellite) project presented a number of challenges to the preparation for mission operations. A class D mission under NASA s risk tolerance scale, LCROSS was governed by a $79 million cost cap and a 29 month schedule from authority to proceed to flight readiness. LCROSS was NASA Ames Research Center s flagship mission in its return to spacecraft flight operations after many years of pursuing other strategic goals. As such, ARC needed to restore and update its mission support infrastructure, and in parallel, the LCROSS project had to newly define operational practices and to select and train a flight team combining experienced operators and staff from other arenas of ARC research. This paper describes the LCROSS flight team development process, which deeply involved team members in spacecraft and ground system design, implementation and test; leveraged collaborations with strategic partners; and conducted extensive testing and rehearsals that scaled in realism and complexity in coordination with ground system and spacecraft development. As a testament to the approach, LCROSS successfully met its full mission objectives, despite many in-flight challenges, with its impact on the lunar south pole on October 9, 2009.

A number of new tactical planning and operations tools were deployed on the highly successful Mars Exploration Rover (MER) mission. Based on successes and lessons from the MER experience, a number of groups at NASA Ames and JPL have developed a platform for developing integrated operations tools, called Ensemble. Ensemble is a multi-mission toolkit for building activity planning and sequencing systems that is being deployed on extended operations for the MER mission, the 2007 Phoenix Mars Lander and the 2009 Mars Science Laboratory rover mission. Experience designing, building and operating the MER tools with our colleagues, studying the use of the MER tools from a Human/Computer Interaction perspective, and feedback from these three missions has lead us to take a somewhat different approach to designing and deploying applications with planning technology this time around. We believe these changes will make future applications even more efficient to use and easier to implement. This experience may be of use and interest to people working on similar kinds of applications, space related or not.

The MAPGEN system represents a successful mission infusion of mixed-initiative planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the Spirit and Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of. the rovers' resources. The Mars Exploration Rover mission has now been operating for more than two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multiyear deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called M-SLICE, that is baselined for the Mars Science Laboratory mission. In this article, we discuss the mixed-initiative aspects of the MAPGEN system, focusing on the task, control, and awareness issues.

Software featuring a multilevel architecture is used to control the hardware on the K9 Rover, which is a mobile robot used in research on robots for scientific exploration and autonomous operation in general. The software consists of five types of modules: Device Drivers - These modules, at the lowest level of the architecture, directly control motors, cameras, data buses, and other hardware devices. Resource Managers - Each of these modules controls several device drivers. Resource managers can be commanded by either a remote operator or the pilot or conditional-executive modules described below. Behaviors and Data Processors - These modules perform computations for such functions as planning paths, avoiding obstacles, visual tracking, and stereoscopy. These modules can be commanded only by the pilot. Pilot - The pilot receives a possibly complex command from the remote operator or the conditional executive, then decomposes the command into (1) more-specific commands to the resource managers and (2) requests for information from the behaviors and data processors. Conditional Executive - This highest-level module interprets a command plan sent by the remote operator, determines whether resources required for execution of the plan are available, monitors execution, and, if necessary, selects an alternate branch of the plan.

Earth scientists require timely, coordinated access to remote sensing resources, either directly by requesting that the resource be targeted at a specific location, or indirectly through access to data that has been, or will be acquired and stored in data archives. The information infrastructure for effective coordinated observing does not currently exist. This paper describes a set of capabilities for enabling model-based observing, the idea of linking scheduling observation resources more directly to science goals. Modelbased observing is realized in this paper by an approach based on concepts of distributed planning and scheduling. The problem raises challenging issues related to planning under uncertainty, monitoring and repair of plans, and reasoning about human objectives and preferences.

This paper describes the software architecture of NASA Ames Research Center s K9 rover. The goal of the onboard software architecture team was to develop a modular, flexible framework that would allow both high- and low-level control of the K9 hardware. Examples of low-level control are the simple drive or pan/tilt commands which are handled by the resource managers, and examples of high-level control are the command sequences which are handled by the conditional executive. In between these two control levels are complex behavioral commands which are handled by the pilot, such as drive to goal with obstacle avoidance or visually servo to a target. This paper presents the design of the architecture as of Fall 2000. We describe the state of the architecture implementation as well as its current evolution. An early version of the architecture was used for K9 operations during a dual-rover field experiment conducted by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL) from May 14 to May 16, 2000.

The MAPGEN system was deployed in the Mars Exploration Rover mission as a mission-critical component of the ground operations system. MAPGEN, which was jointly developed by ARC and JPL, represents a successful mission infusion of planning technology. The MER mission has operated spectacularly for over two years now, and we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations. These lessons have spawned new research in mixed-initiative planning and have influenced the design of a new ground operations system, called ENSEMBLE, that is base-lined for the Phoenix and Mars Science Laboratory missions. This paper discusses some of the lessons learned from the MER mission infusion experience and presents a preliminary report on these subsequent developments.

Earth scientists require timely, coordinated access to remote sensing resources, either directly by requesting that the resource be targeted at a specific location, or indirectly through access to data that has been, or will be acquired and stored in data archives. The information infrastructure for effective coordinated observing does not currently exist. This paper describes a set of capabilities for enabling model-based observing ,t he idea of linking scheduling observation resources more directly to science goals. Model-based observing is realized in this paper by an approach based on concepts of distributed planning and scheduling. The problem raises challenging issues relatedtoplanning under uncertainty, monitoring and repair of plans, and reasoning about human objectives and preferences.

This paper presents an empirical study of some nonexhaustive approaches to optimizing preferences within the context of constraint-based, mixed-initiative planning for mission operations. This work is motivated by the experience of deploying and operating the MAPGEN (Mixed-initiative Activity Plan GENerator) system for the Mars Exploration Rover Mission. Responsiveness to the user is one of the important requirements for MAPGEN, hence, the additional computation time needed to optimize preferences must be kept within reasonabble bounds. This was the primary motivation for studying non-exhaustive optimization approaches. The specific goals of rhe empirical study are to assess the impact on solution quality of two greedy heuristics used in MAPGEN and to assess the improvement gained by applying a linear programming optimization technique to the final solution.