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Cooperative Learning for Robust Connectivity in Multirobot Heterogeneous Networks

Abstract:

By taking advantage of complementary communication technologies, distinct sensing functionalities, and different motion dynamics of a heterogeneous multirobotic system, one can accomplish effectively a mission objective. Furthermore, an adequate coordination of the robotic agents is an important factor to fully exploit the unique capabilities that these systems can offer. In this chapter we focus on a multirobotic network made by aerial relays and ground sensors that is deployed to sense areas of interest in an environment populated with obstacles. We use potential field methods to guide the ground mobile sensors to target regions. On the other hand, antenna diversity and reinforcement learning are combined to control the flying relays such that they increase the received signal strength among them and the mobile sensors. We demonstrate the effectiveness of our control strategy through simulation tests contrasting independent and cooperative learning.