Bio-Inspired Communication for Self-Regulated Multi-Robot Sytems

Abstract

In recent years, the study of social insects and other animals has revealed that collectively, the relatively simple individuals in these self-organized societies can solve various complex and large problems using only a few behavioural rules (Camazine et al., 2001). In these self-organized systems, individual agents may have limited cognitive, sensing and communication capabilities, but they are collectively capable of solving complex and large problems, e.g., coordinated nest construction of honey-bees, collective defence of school fish from a predator attack. Since the discovery of these collective behavioural patterns of self-organized societies, scientists have also observed modulation of behaviours on the individual level (Garnier et al., 2007). One of the most notable self-regulatory processes in biological social systems is the division of labour (DOL) (Sendova-Franks & Franks, 1999) by which a larger task is divided into a number of small subtasks and each subtask is performed by a separate individual or a group of individuals. Task-specialization is an integral part of DOL where a worker does not perform all tasks, but rather specializes in a set of tasks, according to its morphology, age, or chance (Bonabeau et al., 1999). DOL is also characterized by plasticity which means that the removal of one group of workers is quickly compensated for by other workers. Thus distribution of workers among different concurrent tasks keeps changing according to the environmental and internal conditions of a colony. In artificial social systems, like multi-agent or multi-robot systems, the term “division of labour” is often synonymous to “task-allocation” (Shen et al., 2001). In robotics, this is called multi-robot task allocation (MRTA) which is generally identified as the question of assigning tasks to appropriate robots considering changes in task-requirements, environment and the performance of other team members. The additional complexities of the distributed MRTA problem, over traditional MRTA, arise from the fact that robots have limited capabilities to sense, to communicate and to interact locally. In this chapter, we present this issue of DOL as a relevant self-regulatory process in both biological and artificial social systems. We have used the terms DOL and MRTA (or simply, task-allocation) interchangeably. Traditionally, task allocation in multi-agent systems has been dominated by explicit and self-organized task-allocation approaches. Explicit approaches, e.g. intentional cooperation (Parker, 2008), use of dynamic role assignment (Chaimowicz et al., 2002) and market-based bidding approach (Dias et al., 2006) are intuitive, comparatively straight forward to design and implement and can be analysed formally. However, these approaches typically works well only when the number of robots are small (≤ 10) (Lerman et al., 2006). On the other 19