A planning approach to monitor and control for deep space communications

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Abstract
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. This paper describes the application of Artificial Intelligence planning techniques for antenna track plan generation and monitor and control for a NASA Deep Space Communications Station. The described system, CLEaR (Closed Loop Execution and Recovery), will enable an antenna communications station to automatically respond to a set of tracking goals by correctly configuring the appropriate hardware and software and providing the requested communication services, while adapting itself to its dynamic environment. To perform this task, the Continuous Activity Scheduling, Planning, Execution and Replanning (CASPER) engine has been applied and extended to automatically produce antenna tracking plans that are tailored to support a set of input goals. Then during the execution of these track plans, CLEaR monitors the execution and adapts the track plan to the changing environment. In this paper, we will describe the antenna automation problem, the CASPER planning and scheduling system, how CASPER is used to generate antenna track plans and perform monitor and control during execution, and future work utilizing dynamic planning technology.

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