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Optimal workload sharing for mobile robotic networks in dynamic environments

Modern technological advances make the deployment of large groups of autonomous mobile agents with on-board computing and communication capabilities increasingly feasible and attractive. In the near future, large groups of such autonomous agents will be used to perform complex tasks in dynamic environments including transportation and distribution, logistics, surveillance, search and rescue operations, humanitarian demining, environmental monitoring, and planetary exploration [1], [2]. The potential advantages of multiple mobile agents are numerous. For instance, the availability of real-time data collected in situ would dramatically impact the modeling and study of several critical, episodic, rapidly evolving, and localized environmental phenomena, such as hurricanes, oil spills, and forest fires. Similar considerations can be made for the study of ocean currents, winds, and accurate local weather forecasting. Collection of such data is currently limited by the fact that most available sensors are static (e.g., fixed monitoring stations), or remote (e.g., satellites). Moreover, the intrinsic parallelism associated with a multi-agent system provides robustness to failures of single agents, and in many cases can guarantee better time efficiency. It is also possible to reduce the total implementation and operation cost, increase reactivity and system reliability, and add flexibility and modularity to monolithic approaches. As a consequence, the interest of the systems and control, robotics, computer science, and networking communities for systems comprising multiple mobile agents has increased rapidly in the last several years. In particular, the field of multiple Unmanned Aerial Vehicles (UAVs) has gathered a wealth of interest from the research community since UAVs provide ideal platform for several of the above applications [3]. With such a powerful technology at hand, the major scientific challenge is to design efficient coordination policies among these agents. This paper takes the viewpoint that the agents can be interpreted as resources to be allocated to customers. In surveillance and exploration missions, customers are points of interest to be visited; in transportation and logistics applications, customers are people demanding delivery of some goods or service (e.g., utility repair). Finally, consider a possible architecture for networks of autonomous agents performing distributed sensing: a set of n cheap sensing devices (sensing nodes), distributed in the environment, provides sensor measurements, while m sophisticated agents (cluster heads) collect and process information from the sensing nodes