Computational Intelligence - November 2013 - 11

Table 1 ECSS definition of autonomy levels.
level

DescripTion

FuncTions

naming

e1

Mission execution under ground
control; liMited on-board
capability for safety issues
execution of preplanned, grounddefined, Mission operations on
board
execution of adaptive Mission operations on board

real-tiMe control froM ground for noMinal operations. execution of tiMe-tagged
coMMands for safety issues
capability to store tiMe-based coMMands
in an on-board scheduler

real-tiMe control with
preprograMMed sequences

event-based autonoMous operations,
execution of on-board operations
control procedures

seMiautonoMous, also called
"adaptive"

execution of goal-oriented Mission
operations on board

goal-oriented Mission (re-)planning

goal-oriented operation

e3

e4

return rover, Asteroid exploration vehicle to Earth formation flying satellites.
The first article entitled "Efficient
Energy Management for Autonomous
Control in Rover Missions" describes
the results of a study that applies
advanced autonomous reasoning capabilities to robotics planetary exploration.
Since autonomy address challenges like
automated planning, diagnostics, monitoring, and machine learning and so
on, the planetary rover domains represent very relevant and common case
studies. In this article the authors use a
constraint-based heuristic search for
synthesizing complete plan sequences
by reasoning upon a wide set of realistic
requirements and constraints. Starting
from the Mars Sample Return (MSR)
requirements, they define a scheduling
problem, the Power Aware Resource
Constrained Mars Rover Scheduling
(PARC-MRS) problem. Following the
defined problem, the authors formulated
a scheduling algorithm that focuses on a
number of results belonging to previous
research, and provides an extension of a
well-known constraint-based, resourcedriven procedure which exploits poweraware reasoning capabilities within an
integrated resolution strategy, where a
wide variety of complex temporal and
resource constraints are considered, with
special attention to the energy requirements. An exhaustive experimentation
shows the efficiency of the proposed
algorithm, as well as the effectiveness of
an optimization schema in providing
minimum-make span solutions. Another
secondary result of the study is the creation of a meaningful benchmark of
PARC-MRS problem instances tailored
on the MSR domain.

preplanned

Average Speed per Sol
124
120
98
Speed (m/sol)

e2

92

90

90

73
50

48
30
20
1

10
1.2

18.4

19.2

MSL

ExoMars

5.8

Sojourner

MER

SFR

Figure 15 planetary rover exploration speed is improved with the increase of rover autonomy
level (the bottom line shows the average rover speed taking into account locomotion and science sols; top and middle lines only consider locomotion sols, and represent the maximum
average and nominal average rover speed respectively).

The second paper proposes an
approach that utilizes deliberative agentbased architecture integrated with world
model and set of possible actions in terms
of natural language representation with
potential applications in complex environments. The proposed architecture has
been implemented using autonomous
asteroid exploration as a case study. The
agent-based architectures formalize the
relationship between an autonomous system (an agent), operating environments
(that provide percepts) and agent actions
(that may bring about changes in percepts) and hence (if applied in the correct
5

ISBN: 978-84-695-3472-4.

manner) can prove to be a useful paradigm for CI in autonomous space systems development. Several highlights of
the paper include using generic programming, development and deployment platforms, natural language programming
for higher-level symbolic representation,
and performing validation and verification via Gwendolen.
The third paper, "Optimal Satellite
Formation Reconfiguration Based on
Closed-loop Brain Stor m Optimization" applies brain storm optimization (BSO) to the fixed time fuel optimal
reconfiguration of a satellite formation

(continued on page 63)

november 2013 | Ieee ComputatIonal IntellIgenCe magazIne

11



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