Deploying Interactive Mission Planning Tools

2007

Modern planning and scheduling systems are capable of dealing with the size and complexity of many real world problems. However, mission critical planning is still often done by humans. Even if only a couple of plans are produced (“Master Plan” and “Plan B”), human experts evaluate multiple alternatives, think of contingencies, consider the likelihood of failure of various steps, and account for schedule slack and plan flexibility. Computers can evaluate thousands of alternative scenarios, but the solutions they ultimately produce are often not convincing enough for expert decision makers to trust human lives or mission critical operations to computer decisions. Further, automated systems often require significant changes in the way people operate, which in high-stakes high-pressure environments leads to rejection of the system by the users. In this paper we describe the decision support functionality of the Coordinated Multi-source Maintenance on Demand (CMMD) system. CMMD is desig...

SpaceOps 2010 Conference<br> <b><i>Delivering on the Dream</b></i><br> <i>Hosted by NASA Marshall Space Flight Center and Organized by AIAA</i>, 2010

The paper aims at sharing the experience and lessons learnt in introducing advanced planning and scheduling solutions for mission operations at the European Space Agency (ESA). Artificial Intelligence (AI) based technologies are moving from a theoretical area to the application domain, showing the capability of solving many practical problems. In the last years, a number of AI planning and scheduling solutions have been successfully implemented and operationally validated at ESA, bridging the gap between academic and operations worlds. The solutions address different and specific planning or scheduling issues for a variety of ESA mission typology, including science and deep space missions, based on the automatic generations of conflict-free optimal and/or robust plans. The major topics we discuss in the paper are the technology selection process and its rationale, the lessons learnt from the technology infusion process, the flexibility of the solving approach in order to face probable changes in the mission, the robustness of the solutions to cope with the uncertainty or real problems, the incremental solving in order to update current plans, and the integration of the end-user in the planning process to maintain their supervision and exploit thier knowhow.

Telematics and Informatics, 1987

The Evolutionary Definition Office (EDO) at the Langley Research Center (LaRC) has the responsibility to analyze and evaluate alternative growth options of the Space Station and its utilization. Under contract to the EDO, Computer Technology Associates (CTA) has developed a PC-based automated mission and resource planning tool, AUTOPLAN. AUTOPLAN's input is a proposed profile of missions, including for each: start year, number of allowable slip periods, mission duration, and requirement profiles for one or more resources as a function of time. The user also inputs a corresponding availability profile for each resource over the whole time interval under study. Subject to the size of a given problem and microcomputer performance limitations, AUTOPLAN finds all integrated schedules which do not require more than the available resources. AUTOPLAN is implemented in Arity compiled PROLOG and executed on an IBM PC/AT with 640 KB memory. There is particular interest in small-scale planning and scheduling systems in the Space Station program because of the trend toward decentralizing these functions. The iterative resolution and recursion features of PROLOG greatly simplify the programming of this problem and make it easy to customize or generalize the solution evaluation algorithm. The quantitative capabilities of the tool and several postprocessor interpretive aids presently under assessment are described and a realistic sample application of the tool suite is presented.

2003

This paper describes an innovative application of AI technology in the area of space mission planning. A system called Mexar has been developed to synthesize spacecraft operational commands for the memory dumping problem of the ESA mission called Mars Express. The approach implemented in Mexar is centered on constraint satisfaction techniques enhanced with flexible user interaction modalities. This paper describes the effort in developing a complete application that models and solves a problem, and also offers functionalities to help users in interacting with different aspects of the problem. The paper surveys the design principles underlying the whole project and shows how different components contribute to the delivered system.

2011

Mission planning is central to space mission operations and has benefited from advances in model-based planning software, but developing a planning model still remains a difficult task. Mission planning constraints arise from many sources, including simulators and engineering specification documents. Ensuring that these constraints are correctly represented in the planner’s model is a challenge. As mission constraints evolve, planning domain modelers must add and update model constraints efficiently using the available source data, catching errors quickly, and correcting the model. We describe the current state of the practice in designing model-based mission planning tools and the challenges facing model developers. We then propose an Interactive Model Development Environment (IMDE) to configure mission planning systems by integrating modeling and simulation environments to reduce model editing time, generate simulations automatically to evaluate plans, and identify modeling errors...

IEEE Expert / IEEE Intelligent Systems, 2007

Deep-space missions carry an ever larger set of different and complementary onboard payloads. Each payload generates data, and synthesizing it for optimized downlinking is one way to reduce the ratio of mission costs to science return. This is the main role of the Mars-Express scheduling architecture (Mexar2), an Al-based tool in daily use on the Mars-Express mission since February 2005. Mexar2 supports space mission planners continuously as they plan data downlinks from the spacecraft to Earth. The tool lets planners work at a higher abstraction level while it performs low-level, often-repetitive tasks. It also helps them produce a plan rapidly, explore alternative solutions, and choose the most robust plan for execution. Additionally, planners can analyze any problems over multiple days and identify payload overcommitments that cause resource bottlenecks and increase the risk of data losses. Mexar2 has significantly increased the data return over the whole Mars-Express mission duration. It's effectively become a work companion for mission planners at the European Space Agency's European Space Operations Center (ESOC) in Darmstadt, Germany.

2007

Modern planning and scheduling systems are capable of dealing with the size and complexity of many real world problems. However, mission critical planning is still often done by humans. Even if only a couple of plans are produced ("Master Plan" and "Plan B"), human experts evaluate multiple alternatives, think of contingencies, consider the likelihood of failure of various steps, and account for schedule slack and plan flexibility. Computers can evaluate thousands of alternative scenarios, but the solutions they ultimately produce are often not convincing enough for expert decision makers to trust human lives or mission critical operations to computer decisions. Further, automated systems often require significant changes in the way people operate, which in high-stakes high-pressure environments leads to rejection of the system by the users. In this paper we describe the decision support functionality of the Coordinated Multi-source Maintenance on Demand (CMMD) system. CMMD is designed to support the complete life cycle of mission plans for human space exploration, starting with initial long-term planning and ending with day-by-day execution of a detailed schedule. The goal of CMMD is not to replace human experts, but to assist them. To do so, CMMD explains reasons for commitments it makes, allows the user to interactively explore alternatives, guide the search toward more desirable solutions, and to run various queries (e.g., what courses of action have not yet been explored with respect to some goal?). We claim that giving users insight into workings of the system and gradually enhancing existing processes is crucial for gaining user confidence in produced plans and ultimately for adoption of the system.

2015

Modern planning and scheduling systems are capable of dealing with the size and complexity of many real world problems. However, mission critical planning is still often done by humans. Even if only a couple of plans are produced (“Master Plan ” and “Plan B”), human experts evaluate multiple alternatives, think of con-tingencies, consider the likelihood of failure of various steps, and account for schedule slack and plan flexibil-ity. Computers can evaluate thousands of alternative scenarios, but the solutions they ultimately produce are often not convincing enough for expert decision makers to trust human lives or mission critical operations to computer decisions. Further, automated systems often require significant changes in the way people operate, which in high-stakes high-pressure environments leads to rejection of the system by the users. In this paper we describe the decision support func-tionality of the Coordinated Multi-source Maintenance on Demand (CMMD) system. CMMD is d...

1999

On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This paper describes the RAX Planner/Scheduler (RAX-PS), both in terms of the underlying planning framework and in terms of the fielded planner. RAX-PS plans are networks of constraints, built incrementally by consulting a model of the dynamics of the spacecraft. The RAX-PS planning procedure is formally well defined and can be proved to be complete. RAX-PS generates plans that are temporally flexible, allowing the execution system to adjust to actual plan execution conditions without breaking the plan. The practical aspect, developing a mission critical application, required paying attention to important engineering issues such as the design of methods for programmable search control, knowledge acquisition and planner validation. The result was a system capable of building concurrent plans with over a hundred tasks within the performance requirements of operational, mission-critical software.

The ambitious goal of the Spacecraft Monitoring & Control (SM&C) Working Group of the Consultative Committee for Space Data Systems (CCSDS) is to define a set of standardized, interoperable mission operation (MO) services, which allow rapid and efficient construction of cooperating space systems. Such services will have to be general enough to cope with the various needs of existing and future missions, but at the same specified enough to be practically usable. This paper presents some ideas to deal with this difficult task drawn from existing architectures and interfaces used in AI advanced software systems for planning and scheduling.