Computational Intelligence - November 2013 - 13
The proposed approach exploits AI scheduling techniques to
manage temporal and resource constraints within an integrated
power-aware decision-making strategy.
actually exist. The Autonomous Science Experiment (ASE) [2]
onboard autonomous flight software was deployed on the
Earth Observing One (EO-1) spacecraft [3] to enable
autonomous science by exploiting the onboard Continuous
Activity Scheduling Planning Execution and Replanning
(CASPER) [4] planner, generating mission operation plans
and providing rapid responses to a wide range of operation
scenarios. The Mixed-initiative Activity Plan GENerator
(MAPGEN) [5], an automated system which exploits constraint-based planning, scheduling, and temporal reasoning
techniques, was used to support the Mars Exploration
Rover (MER) operations team on the rover activity planning tasks. Another example is represented by the MarsExpress Scheduling Architecture (Mexar2) [6], an AI-based tool
currently in use on the Mars-Express mission to plan data
downlinks from the spacecraft to Earth.
Moreover, there are also many outstanding studies relatively
to the promotion of autonomous technology in robotic missions. For instance, Wood et al [7] present an integration
between the Closed-Loop Execution and Recovery (CLEaR) system and the OASIS onboard science system which supports
onboard decision making capabilities for mission plan generation, execution & monitoring, dynamic re-planning, optimization, and even opportunistic science. The European Space
Agency (ESA) is also fostering similar works in the context of
the forthcoming Exo-Mars [8] rover mission. In this direction,
the ESA's study projects GOAC [9] and IRONCAP [10]
aimed at, respectively, demonstrating key concepts towards fully
autonomous onboard operations for rover-based missions, and
defining the basis for developing a prototype system to support
the whole science and engineering on ground planning activities of an interplanetary rover.
In this work, the Mars Sample Return (MSR) mission
concept [11] is proposed as a plausible and efficient paradigmshift to continue the exploration of the Red Planet in the near
future. Roughly speaking, the MSR mission consists of placing
a rover on Mars' surface, gathering scientific samples from a set
of scattered and challenging sites (up to many kilometers from
the landing site) within relatively short time frames, and transporting them to a specific location where an ascent vehicle
will be in charge of initiating the return trip. The proposed
model encapsulates a wide range of interesting features which
makes it particularly challenging, as it involves: first, global
path-planning, focused on "long-range navigation" planning in
contrast to the classical path-planning research which addresses
"local navigation" to trace safe routes between pairs of loca-
tions separated a few meters apart of each other. Second,
resource management, by analyzing the energy production/
consumption profiles of all the plan activities. Third, a wide
assortment of temporal constraints, such as absolute deadlines
on the experiment execution (e.g., to communicate critical
experimental results via orbiting relays), or rover inactivity
periods (e.g., nights or solar storms) represented as static synchronization events of finite duration.
To this aim, we introduce an MSR-inspired scheduling
problem called Power Aware Resource Constrained Mars Rover
Scheduling (PARC-MRS), and present an extension of a wellknown constraint-based, resource-driven reasoner that returns
rover activity plans as solutions of the PARC-MRS. Our solving process exploits advanced Artificial Intelligence (AI) P&S
constraint-based, resource reasoning techniques, in particular
"Precedence Constraint Posting" (PCP) [12], [13] to reason
upon a detailed model of the PARC-MRS problem instances.
One of the contributions of this work is the exploitation of a
known methodology to represent consumable resources by
means of a cumulative scheme [14], to model and solve the
proposed scheduling problem.
The remainder of the paper is structured as follows: in Section II, we start with a detailed description of the mission scenario of reference. Next, we provide a detailed description of the
extended constraint-based, resource driven reasoner with integrated power-aware decision capabilities. Following that, we
conduct an exhaustive experimentation to evaluate the efficiency
of our solution algorithm and a benchmark instance generator
used to create reproducible PAR-CMRS problem sets. Finally, a
conclusion and future work section closes the paper.
II. The Mars Sample Return Mission Scenario
In this section we provide a definition of the Power-Aware
Resource Constrained Mars Rover Scheduling (PARC-MRS)
problem, that is grounded on the commitment to the Mars
Sample Return (MSR) [11] reliability and efficiency baseline
requirements: the first requirement refers to the need to synthesize plans capable of partially absorbing the effects of possible exogenous events arising during the plan's execution, while
the second refers to the goal of minimizing the plan's completion time, thus maximizing the overall science return.
The attainment of the mission's goals requires the use of the
rover's set of instruments/resources whose utilization must be
synchronized over time in order to guarantee the correct execution of the plan's activities. Each rover activity a i requires a specific amount of one or more resources during its entire execution.
November 2013 | Ieee ComputatIoNal INtellIgeNCe magazINe
13
Table of Contents for the Digital Edition of Computational Intelligence - November 2013
Computational Intelligence - November 2013 - Cover1
Computational Intelligence - November 2013 - Cover2
Computational Intelligence - November 2013 - 1
Computational Intelligence - November 2013 - 2
Computational Intelligence - November 2013 - 3
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