In deep space missions, scientific data generated by onboard instruments must be temporarily stored in local memory buffers before being downlinked to Earth during limited communication windows. Efficient scheduling of these data transfers is essential to prevent buffer overflows and data loss, particularly in the presence of uncertainties. Previous work has considered the overlapping Memory Dumping Problem (oMDP), which consists in assigning transfer priorities to the memory buffers and minimize the peaks memory usage, which reduces the risk of overflow. In this paper, we consider a dditional decisions in the transfer plans that are implementable in practice: data transfer from each buffer can be interrupted after a given time, once per downlink window, preventing it from dumping data until the next window, but redistributing the unused bandwidth to the other buffers. The new problem is called oMDPi (oMDP with interruptions). We obtain new complexity results, showing that oMDPi is NP-complete for at least two windows. While the complexity status of the single window oMDPi remains open, we propose a polynomial-time heuristic to solve it. We propose a hybrid heuristic to solve the general oMDPi, embedding a flow relaxation and a single window heuristic. The results on both real and realistic generated instances show that our heuristic achieves a significant reduction of memory peaks in a reasonable time compared to previous works, making the new policy attractive for future space missions.

Long range space missions, such as Rosetta, require robust plans of data-acquisition activities and of the resulting data transfers. In this paper we revisit the problem of assigning priorities to data transfers in order to maximize safety margin of onboard memory. We propose a fast sweep algorithm to verify the feasibility of a given priority assignment and we introduce an efficient exact algorithm to assign priorities on a single downlink window. We prove that the problem is NP-hard for several windows, and we propose several randomized heuristics to tackle the general case. Our experimental results show that the proposed approaches are able to improve the plans computed for the real mission by the previously existing method, while the sweep algorithm yields drastic accelerations.

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the system of systems.

An Autonomous Science Agent has been flying onboard the Earth Observing One spacecraft since 2003. This software enables the spacecraft to autonomously detect and responds to science events occurring on the Earth such as volcanoes, flooding, and snow melt. This agent includes artificial intelligence software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software is in routine use to fly the EO-1 mission. In this paper we discuss the architecture used to integrate these systems and lessons learned from its multi-year flight on EO-1.

The trend toward multiple-spacecraft missions requires autonomous teams of spacecraft to coordinate their activities when sharing limited resources. The paper describes how an iterative repair planner/scheduler can reason about the activities of multiple spacecraft at abstract levels in order to greatly improve the scheduling of their use of shared resources. By finding consistent schedules at abstract levels, refinement choices can be preserved for use in robust plan execution systems. We present an algorithm for summarizing the metric resource requirements of an abstract activity based on the resource usages of its potential refinements. We find that reasoning about this summary information and that of state constraints can offer exponential improvements in the time to find consistent schedules with an iterative repair planner. We analytically describe the conditions under which these improvements are made and show that sometimes the extra overhead involved does not warrant their use. We apply these techniques within the ASPEN planner/scheduler to a domain where a team of rovers must coordinate their schedules to avoid conflicts over shared resources. Experiments using the ASPEN planner/scheduler in a Mars multi-rover domain support our analyses and compare techniques for controlling decomposition.

This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences. This prototype is based on ASPEN (Automated Scheduling and Planning Environment). This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute: within resource constraints and satisfy flight rules. An automated planning and scheduling system encodes rover design knowledge and uses the search and reasoning techniques to automatically generate low-level command sequences while respecting the rover operability constraints. This prototype planning system has been field-tested using the Rocky-7 rover at JPL, and will be field-tested on more complex rovers to prove its effectiveness before transferring the technology to flight operations for an upcoming NASA mission. The goal-driven commanding of planetary rovers greatly reduces the requirements for highly skilled rover engineering personnel. This in turn greatly reduces mission operations costs and permits a faster response to changes in rover states.

Automated planning and scheduling, including automated path planning, has been integrated with an Internet-based distributed operations system for planetary rover operations. The resulting prototype system enables faster generation of valid rover command sequences by a distributed planetary rover operations team. The Web Interface for Telescience provides Internet-based distributed collaboration, the automated scheduling and planning environment provides automated planning and scheduling, and an automated path planner provides path planning. The system was demonstrated on the Rocky7 research rover at JPL.