multi-robot system requires particular support, and the above mentioned examples show, that autonomy is not only desirable for nominal operations, but also for maintenance: the possibil-ity to extend or refurbish existing hardware is an advantage, even more when a robot team can perform this process autonomously.
The modular design of reconfigurable multi-robot systems will initially increase the cost of development, but serves as key to new mission concepts and cost reductions in the long run by enabling reuse of components. Attributes of swarm-based systems can be exploited in the sense, that when robots share hardware resources, the risk of failure can easier be mitigated.
In effect, a higher risk of failure per device can be tolerated, when the redundancy increases.
Therefore, device development might be cheaper leading to a reduction to overall mission cost.
This benefit increases when the technology for in-situ creation of repair structures and devices advances (ESA2018a) is available for space missions and allows to safe launch costs.
2.3.1 Autonomous Operation
The aim of increasing autonomy in space missions is the result of “pressure to reduce man-power during routine mission operations” (European Cooperation for Space Standardization 2008a). Truszkowski, Hinchey, and Rash (2004) favour swarm-based approaches to use as a solution and argue that an application of “large number of spacecraft provide[s] greater flex-ibility, reliability and autonomy than the more familiar spacecraft”. They judge, however, the verifiability of these approaches as major limitation, which still persists according to Vassev et al. (2012). While Truszkowski, Hinchey, and Rash (2004) refer to spacecrafts, their argu-ment applies as well to planetary space robots. Bajracharya, Maimone, and Helmick (2008) see that even minimal mission success requires an increased level of autonomy: sample re-turn missions will require ascent vehicles, which come with a limited lifetime, increasing the pressure to operate sampling collecting rovers with higher speeds. In this context, however, the communication channel limits direct control schemes. The Mars Science Laboratory has a data rate in the range of 500 to 32,000 bps (NASA2018). Interaction between ground sta-tions and robotic systems involves satellite communication and comes with long latency: to communicate a message between Earth and Mars takes an approximate time between 4 and 24 minutes (ESA2018b). Additionally, using theDeep Space Network (DSN)(Washington et al.
1999) limits the communication to time windows and hence fragments a direct operation.
To maximise the use of deployed space robots theEuropean Collaboration for Space Standard-ization (ECSS) already considers the availability of the ground station for space robots and
Figure 2.7:Schematic description of an incremental design of planetary space missions using reconfigurable multi-robot systems.
Table 2.4: Autonomy levels for nominal mission operation according to the ECSS (European Cooperation for Space Standardization2005).
Level Description Functions
E1 Mission execution under ground control; limited onboard capability for safety issues
Real-time control from ground for nominal op-erations, Execution of time-tagged commands for safety issues
E2 Execution of pre-planned, ground defined, mis-sion operations on-board
Capability to store time-based commands in an on-board scheduler
E3 Execution of adaptive mission operations on-board
Event-based autonomous operations, Execution of on-board operations control procedures
E4 Execution of goal-oriented mission operations on-board
Goal-oriented mission re-planning
requires such robots to tolerate a so-called “non-availability period“. TheECSSclassifies au-tonomous operation into levels of autonomy according to Table2.4(European Cooperation for Space Standardization2005, Section 5.7). Currently, no space rover is deployed on a remote planet which supports an autonomy level E4. Only the rover ExoMars is expected to show full autonomy (E4) with an average driving speed of 14 m/h (Gao et al.2016; Winter et al.2015).
TheECSSalso accounts for the autonomy level for failure handling (see Table2.5) which de-fines the need for fault tolerant systems with limited self-repair capabilities. The autonomous fault handling levels categorise system resilience (cf. Section2.1), and clearly refer to reconfig-uration as a major means to perform failure handling. Particularly modular and redundantly designed systems can support the demands of F1 to reconfigure systems and isolate faulty components. Furthermore, they can enable advanced (re)planning approaches for F2.
Increasing autonomy for robotic systems is part of the Global Exploration Roadmap defined by the International Space Exploration Coordination Group (ISEG) (International Space Ex-ploration Coordination Group 2013). The ISEG also looks at affordability, capability evolu-tion, and robustness among other aspects as mission driving principles and thereby implicitly stresses the relevance of reconfigurable multi-robot systems for future space missions. How-ever, while commercialisation in space industry is already starting in this area, e.g., with the iBOSSsystem as basis for supporting reconfigurable satellites, an exploitation of the flexibil-ity of this reconfigurable multi-robot system hardware still demands modelling and planning approaches, as well as a supporting software architecture that allows autonomous operation of such systems.
2.3.2 Dynamic Team Operations
Robotic exploration missions might require the participating robots to react to unforeseen events and changing priorities. Reconfigurable multi-robot systems are able to adapt to the demands of robotic operations by changing their morphologies and their overall coalition structure. Depending on the time and cost of reconfiguration of an overall team, dynamically changing teams enable new operation schemes. Their flexibility maintains alternative ways to achieve mission goals under adversarial circumstances. In effect, they offer potential for a safer autonomous operation compared to monolithic systems.
To support long-term missions, as well as dynamically changing requirements, new (or
dynam-Table 2.5:Autonomy levels for failure handling according to the ECSS (European Cooperation for Space Standardization2005).
Level Description Functions
F1 Establish safe space segment configuration follow-ing an onboard failure
Identify anomalies and report to ground segment, reconfigure on-board systems to isolate failed equipment or functions, place space segment in a safe state
F2 Re-establish nominal mission operations follow-ing an on-board failure
As F1, plus reconfigure to a nominal operational configuration, resume execution of nominal oper-ations, resume generation of mission products
ically created) agents have to be included and accounted for in the overall multi-robot system.
A new agent can add capabilities to the overall system. This can only be exploited, when the given software infrastructure and high-level planning mechanisms can account for these new capabilities. Hence, interoperation and extensibility of a reconfigurable multi-robot system depend upon a significant level of standardisation. Additionally, model-based development approaches and model-based reasoning can support a generic infrastructure and automation approaches as described in Chapter3: theEMIdeveloped in RIMRES and TransTerrA is only one interface, and a reconfigurable multi-robot system might also use multiple variants.
Dynamically changing coalitions are observable in two situations. Firstly, as result of a coali-tion structure change, e.g., triggered to fulfil changing funccoali-tional needs or to address safety issues. Secondly, when the overall number of available agents changes; either through loss or addition of individual agents. The requirements arising from both variants demand a transpar-ent mechanism of adding and removing robotic systems. The coalition structure might change disruptively, i.e., leaving some robots unpowered. These requirements and an intended appli-cation for space exploration in unknown or partially known environments suggest applying a distributed communication approach. This approach comes with the benefit of enabling local and self-sustained operative coalitions: a subteam of agents can remain operational indepen-dent of the communication to other agents.