Synchronous and Asynchronous Communication Modes for Swarm Robotic Search

Swarm robots are special multi-robots and usually considered being controlled with swarm intelligence-basedmethod to complete some assigned complex tasks (Dorigo and Sahin, 2004). Similar to the biological counterparts in nature, swarm intelligence among such artificial system is emerged from local interactions between individual robots or individual robot and its environment (Beni, 2005; Şahin, 2005). It is obvious that interactions play a crucial role in emergence of swarm intelligence in swarm robotics (Schmickl and Crailsheim, 2008). In other words, communication mode taken in control process of swarm robotic search is important. How to control swarm robots with certain communication mode? We can borrow ideas from swarm intelligence-based optimization algorithms in general, and the particle swarm optimization (PSO) algorithm in particular, since the case of swarm robotic search can be mapped to the case of functions optimization with PSO. Later, this method is named as the extended particle swarm optimization (EPSO) method (Pugh and Martinoli, 2007). The particle swarm optimization algorithm is a global, stochastic search one, being derivative-free and population-based style (Schutte et al., 2004). As one of tools of systemic modeling and cooperative control, it can be used to model swam robotic systems and control robots cooperatively. Bio-inspiringly, this algorithm works in parallel in nature. Learning from this, we can control swarm robotic search with special communication modes in similar way. As for the parallel algorithms, they can be classified by granularity (Xu and Zeng, 2005). Wang et al. (2007) present a parallel version of PSO based on parallel model with controller. Its communication cycle affects speedup of the algorithm. Huang and Fan (2006) propose parallel version of PSO by island population modeling. It partitions the group into several sub-groups and places them on different processors to evolve, communicating timely in the evolution procedures. Zhao et al. (2005) introduce an idea of migration into PSO, present a parallel version based on multi-groups evolving simultaneously. All sub-groups are collected to get the optima by comparison after several iterations. Then the particle having best