scispace - formally typeset
Search or ask a question
Author

Darin Koblick

Bio: Darin Koblick is an academic researcher from Northrop Grumman Corporation. The author has contributed to research in topics: Space exploration & United States Space Surveillance Network. The author has an hindex of 1, co-authored 2 publications receiving 17 citations.

Papers
More filters
Proceedings ArticleDOI
12 May 2008
TL;DR: In this article, the authors present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work, along with the flexibility of ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission.
Abstract: The Orbital Express space mission was a Defense Advanced Research Projects Agency (DARPA) lead demonstration of on-orbit satellite servicing scenarios, autonomous rendezvous, fluid transfers of hydrazine propellant, and robotic arm transfers of Orbital Replacement Unit (ORU) components. Boeing's Autonomous Space Transport Robotic Operations (ASTRO) vehicle provided the servicing to the Ball Aerospace's Next Generation Serviceable Satellite (NextSat) client. For communication opportunities, operations used the high-bandwidth ground-based Air Force Satellite Control Network (AFSCN) along with the relatively low-bandwidth GEO-Synchronous space-borne Tracking and Data Relay Satellite System (TDRSS) network. Mission operations were conducted out of the RDTE autonomous free-flyer capture was demonstrated on June 22, 2007; the fluid and ORU transfers throughout the mission were successful. Planning operations for the mission were conducted by a team of personnel including Flight Directors, who were responsible for verifying the steps and contacts within the procedures, the Rendezvous Planners who would compute the locations and visibilities of the spacecraft, the Scenario Resource Planners (SRPs), who were concerned with assignment of communications windows, monitoring of resources, and sending commands to the ASTRO spacecraft, and the Mission planners who would interface with the real-time operations environment, process planning products and coordinate activities with the SRP. The SRP position was staffed by JPL personnel who used the Automated Scheduling and Planning ENvironment (ASPEN) to model and enforce mission and satellite constraints. The lifecycle of a plan began three weeks outside its execution on-board. During the planning timeframe, many aspects could change the plan, causing the need for re-planning. These variable factors, ranging from shifting contact times to ground-station closures and required maintenance times, are discussed along with the flexibility of the ASPEN tool to accommodate changes to procedures and the daily or long-range plan, which contributed to the success of the mission. This paper will present an introduction to ASPEN, a more in-depth discussion on its use on the Orbital Express mission, and other relative work. A description of ground operations after the SRP deliveries were made is included, and we briefly discuss lessons learned from the planning perspective and future work.

17 citations


Cited by
More filters
Proceedings ArticleDOI
11 Jun 2012
TL;DR: A basic timeline representation that can represent a set of state, resource, timing, and transition constraints is described and a number of planning and scheduling systems designed for space applications are described and described how they do and do not map onto this timeline model.
Abstract: Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.

75 citations

Proceedings Article
12 May 2010
TL;DR: A greedy heuristic scheduling algorithm is described and its performance is compared to both the prior scheduling algorithm and a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.
Abstract: We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.

63 citations

Proceedings ArticleDOI
04 Jan 2016
TL;DR: In this article, the impact of in-space assembly is discussed to identify gaps in structural technology and opportunities for new vehicle designs to support NASA's future long-duration missions to support scientific observatories and human/robotic Mars exploration.
Abstract: As NASA exploration moves beyond earth's orbit, the need exists for long duration space systems that are resilient to events that compromise safety and performance. Fortunately, technology advances in autonomy, robotic manipulators, and modular plug-and-play architectures over the past two decades have made in-space vehicle assembly and servicing possible at acceptable cost and risk. This study evaluates future space systems needed to support scientific observatories and human/robotic Mars exploration to assess key structural design considerations. The impact of in-space assembly is discussed to identify gaps in structural technology and opportunities for new vehicle designs to support NASA's future long duration missions.

50 citations

Proceedings Article
25 Jul 2015
TL;DR: The ESA Rosetta Science Ground Segment has developed a science scheduling system that includes an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter and how this software is used is focused on.
Abstract: Rosetta is a European Space Agency (ESA) cornerstone mission that entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014 and will escort the comet for a 1.5 year nominal mission offering the most detailed study of a comet ever undertaken by humankind. The Rosetta orbiter has 11 scientific instruments (4 remote sensing) and the Philae lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment has developed a science scheduling system that includes an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While automated scheduling is a small portion of the overall Science Ground Segment (SGS) as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called ASPEN-RSSC) and how this software is used.

20 citations

Journal ArticleDOI
TL;DR: The planning problem is formulates, how EUROPA solves the problem is explained, and performance statistics from several planning scenarios are provided.
Abstract: Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans. SACE further provides flight controllers with real-time situational awareness and what-if analysis capabilities. SACE is built on the Extensible Universal Remote Operations Planning Architecture (EUROPA) model-based planning system. EUROPA facilitated SACE development by providing model-based planning, built-in constraint reasoning capability, and extensibility. This article formulates the planning problem, explains how EUROPA solves the problem, and provides performance statistics from several planning scenarios. SACE reduces a highly manual process that takes weeks to an automated process that takes tens of minutes.

16 citations