Learning by Experience and by Imitation in Multi-Robot Systems

With the increasing number of robots in industrial environments, scientists/technologists were often faced with issues on cooperation, coordination and collaboration among different robots and their self governance in the work space. This has led to the development of systems with several cooperative robotic agents. (Kim et al., 1997b). Generally, a system with several robotic agents (multi-robot system) is composed by two or more robots executing a task in a cooperative way (Arai and Ota, 1992). Coordination, collaboration and cooperation are three terms used without distinction when working with multi-agent and multi-robot systems. In this work, we adopt a definition proposed by Noreils (Noreils, 1993) in which cooperation occurs when several agents (or robots) are gathered together so as to perform a global task. Coordination and collaboration are two forms of cooperation (Botelho and Alami, 2000). Coordination occurs when an entity coordinates its activity with another, or it synchronizes its action with respect to the other entity, by exchanging information, signals, etc. And, collaboration occurs when two or more agents decompose a global task in subtasks to be performed by each specific agent. Generally, the solution for problems using multi-agent and multi-robot systems is divided into stages. When talking about autonomous robots, two of these stages are the task allocation stage and the task execution stage. Task allocation should be done so that all components (agents or robots) in the system are used and the problem is completely solved. The task execution stage itself should be performed so that the agents do not interfere to each other (coordination and/or collaboration) when solving the problem. Traditionally, both stages are carried out independently. In the task allocation stage, it is defined if the agents will collaborate to each other or if they will coordinate their activities. In the task execution stage, collaboration and/or coordination are effectively done. In the literature, each stage is implemented using different techniques. The task allocation stage can be implemented using centralized or decentralized approaches (Le Pape, 1990). Centralized approaches can be implemented as an optimization problem. Decentralized approaches generally use marked based approaches like the contract-net protocol (CNP) (Ulam et al., 2007) or other approaches derived from it (Botelho and Alami, 1999). The task execution stage can be implemented in many ways. It depends of the nature of interactions between agents (Collaboration or coordination) and if agents can modify or not their strategies (Static and dynamic strategies). For example it can be implemented using