As mobile robots become increasingly autonomous over extended periods of time, opportunities arise for their use on repetitive tasks. We define and implement behaviors for a class of such tasks that we call continuous area sweeping tasks. A continuous area sweeping task is one in which a group of robots must repeatedly visit all points in a fixed area, possibly with nonuniform frequency, as specified by a task-dependent cost function. Examples of problems that need continuous area sweeping are trash removal in a large building and routine surveillance. In our previous work we have introduced a single-robot approach to this problem. In this paper, we extend that approach to multi-robot scenarios. The focus of this paper is adaptive and decentralized task assignment in continuous area sweeping problems, with the aim of ensuring stability in environments with dynamic factors, such as robot malfunctions or the addition of new robots to the team. Our proposed negotiation-based approach is fully implemented and tested both in simulation and on physical robots

One of the main requisites for multi agent researchers is a multi-purpose infrastructure that enables them to create, test and evaluate protocols, methods and algorithms. RoboCupRescue simulation environment is a flexible and configurable environment that can fill this gap. With changes in its configuration files, it will gain different features, so different methods can be designed, implemented, and tested. This environment is actually suitable for most multi-agent research issues such as cooperation, coordination, agent communication languages, and agent micro level issues. Sensible roles and tasks in the system motivate more research and make this environment a suitable infrastructure for educational purposes. Arian is a set of implemented agents for RoboCupRescue system. Using Arian agents will speed up implementing agents for this environment. In this paper, we introduce RoboCupRescue simulation environment and Arian agents along with some examples of research issues in this environment. We also present some of RoboCupRescue sub-problems for specific research goals. Although RoboCupRescue can be considered as an application of multi-agent research, in this paper we focus on the suitability of this environment as an infrastructure for multi-agent research and education.