Computational Intelligence for Space Systems and Operations [Guest

Guest Editorial

Yang Gao University of Surrey, United Kingdom Nicola Policella ESOC-ESA, Germany Frank Kirchner DFKI, Germany

Computational Intelligence for Space Systems and Operations

C

urrent and future space missions require an increasing level of autonomy or intelligence distributed across the space systems that have computing capabilities to implement intelligent functionalities for decision making. Such computational intelligence (CI) allows spacecraft (vehicles and robots alike) to respond rapidly to opportunistic events in deep space when remote operations are not practical due to communication latency, or to enable ground operators to optimize complex mission (e.g. involving multiple spacecraft) planning and scheduling processes, and so on. Typical CI approaches that can be used to improve spacecraft autonomy include mathematical, probabilistic and statistical modeling, control, automation and optimization, safety and reliability, system identification, monitoring and fault detection, etc. There are therefore strong motivations to develop these expertise areas for answering to the research challenges posed by astronautics and space engineering. The ECSS1 has defined four level of autonomy for a space system, ranging from direct operator control of all actions to the system’s control of decision-making and action with only subsequent human intervention (see detailed definition in Table 1). Although space system autonomy is often assimilated to 1 The European Committee for Space Standardization (ECSS) was created in 1993 as the European organization responsible for the creation and publishing of standards for space projects. See http://ecss.nl/.

Digital Object Identifier 10.1109/MCI.2013.2279557 Date of publication: 16 October 2013

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The IEEE Task Force on Intelligent Space Systems and Operations aims to identify design requirements and drivers of CI approaches for space, and drive in particular original and theoretical research in CI for space systems/ operations where reliability and robustness are key. its space (or on-board) segment, the ground segment can play a crucial role as well. If performance of a space system need to be assessed, it is necessary to conceive the system as a whole and consider implications for the ground segment when the level of autonomy at the space segment is increased. In addition, a sufficient level of autonomy within the ground segment is often required by the operations of autonomous spacecraft. In the past two decades, major space agencies have undertaken development in intelligent software employing CI techniques that can demonstrate high autonomy level (i.e. E4 according to the ECSS standard). For example, NASA 2 has designed goal-oriented planners such as EUROPA, ASPEN and CASPER, which subsequently led to real mission operation software RAX and ASE, etc. More recent research endeavor is to extend and apply these designs to address more complex space systems involving multiple spacecraft, such as the MISUS project for multi-rovers. ESA3 has also funded development of the planning software framework APSI, and design studies GOAC and IRONCAP built on APSI framework for future planetary rover missions. 2 3

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Major research activities have also been carried out by space industries, research institutes and universities all over the world. Taking Mars exploration missions as an example, the increase in autonomy level of the rover systems (from Sojourner of NASA in 1997 to future ESA ExoMars and sample fetch rovers) has noticeably improved rover traverse rate per sol as illustrated in Figure 1. The IEEE Task Force on Intelligent Space Systems and Operations4 was set up within the IEEE Computational Intelligence Society to promote the research, development, education and understanding of applications of computational intelligence in space systems and operations. The major goals include establishing more clear definitions of the emerging field, identifying design requirements and dr ivers of CI approaches for space, and driving in particular original and theoretical research in CI for space systems where reliability and robustness are key. This special issue aims to present latest research work from the scientific community that tackle space engineering problems using CI techniques. The three articles selected from twenty submissions cover a good range of applications on Mars sample 4

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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“

goal-oriented mission (re-)planning

goal-oriented operation

e3

e4

Execution of goal-oriented mission operations on board

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

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* 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014)

Guest Editorial   (continued from page 11)

December 9–12, 2014 Place: Orlando, Florida, USA General Chair: HaiBo He http://www.ieee-ssci.org/

modeled using the conventional disturbance free Hill Clohessy Wiltshire equations. The approach is applied in a closed loop and incorporates obstacle avoidance. This particular problem before imposing obstacle avoidance is linear and does have an analytic optimal solution, which might be more advantageous in practice. However, applying BSO to this simplified model makes adopting the technique simpler for a more complex formation reconfiguration scenario with the modeling of external perturbations. The proposed closed-loop BSO approach improves convergence rate and optimization performance compared to PSO (Particle Swarm Optimization). The CPU time is also improved compared to conventional BSO techniques. In summary the three papers within the special issue provide a good snapshot of representative academic research in CI for space systems and operations. We would like to thank the authors and the reviewers for their contributions to this special issue. Moreover, we are grateful for the IEEE Computational Intelligence Magazine for the opportunity to publish the special issue and the Editor-in-Chief for his insightful feedback, support and guidance in the publication. 

* 2015 IEEE Conference on Computational Intelligence in Games (IEEE CIG 2015)

August 31–September 2, 2015 Place: Tainan, Taiwan General Co-Chairs: I-Chen Wu, Chang-Shing LEE, and Shun-Chin Hsu Website: TBD

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