Artificial Intelligence
17 Apr 2006

Swarm Intelligence

Decentralized and local cooperation is a key element of swarm behaviour.
Decentralized and local cooperation is a key element of swarm behaviour.

Does a coherent group behaviour require an explicit mechanism of cooperation? Can useful tasks be accomplished by heterogeneous agents without direct communication and using decentralized control?

Study description

The space environment typically puts stringent constraints on the capabilities of single satellites, robots or anything that needs to survive in space (space agents). Space agents are particularly limited in terms of mobility (propellant and power limited), communication (power limited) and size (mass limited). At the same time a high level of adaptability, robustness and autonomy is required to increase the chances of success of operating in a largely unknown environment. Similar characteristic are found in the individual components of a biological swarm.

Moreover, a number of space applications are naturally based on the presence of multiple space agents. The first commercial application proposed and realized for satellite systems was that of Arthur C. Clarke and was a satellite constellation providing global communication services by means of three satellites put in a geostationary orbit [1].

Sequence of a self-assembling swarm of space agents.
Sequence of a self-assembling swarm of space agents.

Since then, a large number of constellations have been deployed to provide global communication, navigation and Earth observation services. More recently, the idea of a number of satellites flying in formation has been used in a number of missions for applications ranging from X-Ray astronomy (XEUS), differential measurements of the geomagnetic field (CLUSTER II), space interferometry, the search for exoplanets (DARWIN) and others. All these missions are able to meet their requirements without making use of an emerging property that can be seen as swarm intelligence. On the other hand, if available, swarm intelligence methods would represent an attractive design option allowing, for example, to achieve autonomous operations of formations. Simple agents interacting locally could be considered as a resource, rather than as a overhead. At the same time one would be able to engineer systems that are robust, autonomous, adaptable, distributed and inherently redundant. Besides, swarms allow for mass production of single components, thus promising for mission cost reduction, and represent highly stowable systems, thus allowing to reduce launch costs. These motivations led recently a number of researchers to simulate some degree of swarm intelligence in a number of space systems and to investigate their behaviour.

When a swarm of homogeneous agents is considered and the task is given to acquire a certain final geometry, the final positions occupied by each agent in the target configurations should be chosen in an autonomous way and should be part of the global behaviour emerging from the individual tasks assigned. This result is actually possible using a technique developed at the ACT [2,3] and inspired by swarm aggregation results [4] for terrestrial robots. Introducing a behavioural component that accounts for the differential gravity typical of orbital environments, the algorithm allows, in a given countable number of final formations, to obtain a swarm whose emerging behaviour is the solution of the target allocation problem and the acquisition and mainteinance of the final formation. A behaviour-based control approach for satellites swarms has also been shown to be useful in controlling highly non linear systems such as those deriving by introducing electrostatic interactions between swarm agents [5].

References

  1. A.C. Clarke. Extra terrestrial relays. Wireless World, pages 305-308, 1945.
  2. D. Izzo and L. Pettazzi. Autonomous and distributed motion planning for satellite swarm. Journal of Guidance Control and Dynamic, Vol 30, No.2, pp 449-459, 2007.
  3. D. Izzo, L. Pettazzi and D. Girimonte. A Path Palnning Technique Applied To Satellite Swarm, In Proceedings of the workshop on "Artificial Intelligence for Space Applications at IJCAI07."
  4. V. Gazi. Swarm aggregations using artificial potentials and sliding mode control. In Proceedings of the IEEE Conference on Decision and Control, pages 28482853, Maui,Hawaii, December 2003.
  5. L. Pettazzi, D. Izzo, and S. Theil. Swarm navigation and reconfiguration using electrostatic forces. In Proceedings of the 7th International Conference On Dynamics and Control of Systems and Structures in Space, pages 257268, 2006.

Outcome

Mission Analysis Peer reviewed article
Autonomous and Distributed Motion Planning for Satellite Swarm
Izzo, D. and Pettazzi, L.
Journal of Guidance Control and Dynamics 30, no. 2: 449-459
(2007)
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BibTex
Mission Analysis Conference paper
Swarm navigation and reconfiguration using electrostatic forces
Pettazzi, L. and Izzo, D. and Theil, S.
7th International Conference On Dynamics and Control of Systems and Structures in Space (DCSSS). The Old Royal Naval College, Greenwich, London, England
(2006)
Download
BibTex
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Advanced Concepts Team