Are (explicit) multi-robot coordination and multi-agent coordination really so different?

Largely because of significant qualitative differences between the respective systems under study, the multi-agent and multi-robot research communities have each developed their own methods for perception, reasoning, and action in individual agents/robots. In particular, the multi-robot community has historically studied both implicit and explicit coordination techniques. Implicit coordination techniques employ dynamics of interaction among the robots and the environment in order to achieve the desired collective performance, often in the form of designed emergent behavior. These methods, while often very elegant and efficient, have so far defied general analysis, but show great promise in particular for large-scale teams of simple individuals, and are being studied actively in robotics as well as in several other fields (including mathematics, artificial life, etc.). Explicit coordination techniques, in comparison, deal with comparatively more sophisticated agents/robots, and employ intentional communication and collaboration methods much like those employed in multi-agent systems. Therefore, at the level of explicit coordination among multiple individuals, the differences between techniques used in multi-agent systems (MAS) and those used in multi-robot systems (MRS) are in fact very few. This is not to say that MAS and MRS are equivalent in any fundamental way, but rather that although robotics researchers employ sophisticated specialized techniques of various sorts (e.g., controltheoretic (Hao, Laxton, Agrawal, Lee & Benson 2003, Pereira, Das & Kumar 2003), probabilistic (Riley & Veloso 2002, Thomas Röfer and Matthias Jüngel 2003)) when designing single-robot control systems, they have so far tended to use techniques that are already well-known in the agent community when designing explicitly coordinated MRS. The discussion in the rest of this paper deals with explicitly coordinated MRS, so MRS can heretofore be assumed to refer to such systems and to exclude implicit MRS. In our previous work (Gerkey & Matarić 2003a), we examined the similarity among existing MRS in some depth, focusing on the problem of multi-robot task allocation (MRTA). We showed that many of the MRTA architectures that can be found in the literature are in fact solving well-understood optimization problems (e.g., the Online Assignment Problem (Kalyanasundaram & Pruhs 1993)) using well-understood techniques (e.g., the canonical Greedy Algorithm (Ahuja, Magnanti & Orlin 1993)). We also offered a theoretically sound, non-economic explanation for the success of market-based task allocation mechanisms (e.g., (Dias & Stentz 2001), (Gerkey & Matarić 2002)) based on well-known results from linear programming (Gale 1960). In a recent extension to our analysis (Gerkey & Matarić 2003b), we developed a taxonomy of MRTA problems, dividing the space along three axes: