Barbara Engelhardt

A planning approach to monitor and control for deep space communications

In recent years with the large increase in the number of space missions at NASA and JPL (Jet Propulsion Laboratory), the demand for deep space communications services to command and collect data from these missions has become more difficult to manage. In an attempt to increase the efficiency of operating deep space communications antennas, we are developing a prototype system to perform monitor, control, execution and recovery in order to automate the operations of the Deep Space Network (DSN) communication antenna stations. The authors describe the antenna automation problem, the GASPER planning and scheduling system, how GASPER is used to generate antenna track plans and perform monitor and control during execution, and future work utilizing dynamic planning technology.

The RADARSAT-MAMM Automated Mission Planner

The RadarSAT Modified Antarctic Mapping Mission (MAMM) ran from September to November 2000. It consisted of over 2400 synthetic aperture radar (SAR) data takes over Antarctica that had to satisfy coverage and other scientific criteria while obeying tight resource and operational constraints. Developing these plans is a time and knowledge intensive effort. It required over a work-year to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of “what-if” scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.

Balancing deliberation and reaction, planning and execution for space robotic applications

Intelligent behavior for robotic agents requires a careful balance of fast reactions and deliberate consideration of long-term ramifications. The need for this balance is particularly acute in space applications, where hostile environments demand fast reactions, and remote locations dictate careful management of consumables that cannot be replenished. However, fast reactions typically require procedural representations with limited scope and handling long-term considerations in a general fashion is often computationally expensive. We describe three major areas for autonomous systems for space exploration: free-flying spacecraft, planetary rovers, and ground communications stations. In each of these broad applications areas, we identify operational considerations requiring rapid response and considerations of long-term ramifications. We describe these issues in the context of ongoing efforts to deploy autonomous systems using planning and task execution systems.

An architecture for an autonomous ground station controller

The Deep Space Station Controller (DSSC) is a state of the art ground station control architecture being developed at the JPL. The DSSC has been designed for robust closed loop control of ground communication stations utilized for communications with and commanding of NASAs deep space exploration missions.

Onboard autonomy software on the Three Corner Sat mission

Three Corner Sat (3CS) is a mission of 3 university nanosatellites scheduled for launch in late 2002. The 3CS mission will utilize significant autonomy to improve mission robustness and science return. The 3CS mission will use onboard science data validation, responsive replanning, robust execution, and anomaly detection based on multiple models. Flight of these revolutionary technologies will enable new opportunities in space-borne science and space exploration.

Empirical Evaluation of Local Search Methods for Adapting Planning Policies in a Stochastic Environment

Optimization of expected values in a stochastic domain is common in real world applications. However, it is often difficult to solve such optimization problems without significant knowledge about the surface defined by the stochastic function. In this paper we examine the use of local search techniques to solve stochastic optimization. In particular, we analyze assumptions of smoothness upon which these approaches often rely. We examine these assumptions in the context of optimizing search heuristics for a planner/scheduler on two problem domains. We compare three search algorithms to improve the heuristic sets and show that the two chosen local search algorithms perform well. We present empirical data that suggests this is due to smoothness properties of the search space for the search algorithms.

Hypothesis generation strategies for adaptive problem solving [spacecraft mission control]

Proposed missions to explore comets and moons will encounter environments that are hostile and unpredictable. Any successful explorer must be able to adapt to a wide range of possible operating conditions in order to survive. The traditional approach of constructing special-purpose control methods would require information about the environment, which is not available a priori for these missions. An alternate approach is to utilize a general control approach with significant capability to adapt its behavior, a so called adaptive problem-solving methodology. Using adaptive problem-solving, a spacecraft can use reinforcement learning to adapt an environment-specific search strategy given the crafts general problem solver with a flexible control architecture. The resulting methods would enable the spacecraft to increase its performance with respect to the probability of survival and mission goals. We discuss an application of this approach to learning control strategies in planning and scheduling for three space mission models: Space Technologies 4, a Mars Rover, and Earth Observer One.