An auction-based resource allocation strategy for joint-surveillance using networked multi-robot systems

Networked multi-robot systems benefit from a large amount of heterogeneous online data on the server, and enable poor-equipped robots to fulfill complex tasks. However, as a major bottleneck of practical network, the limited bandwidth is lack of consideration. In the matter of fact, resource competition is pervasive for practical networked robotic applications. We propose a multi-robot negotiation mechanism in this paper. It includes a game theory based auction for allocating resources that are shared among robot clients, such as the network bandwidth. We validate the proposed strategy by a joint-surveillance scenario. Experimental results demonstrate that the proposed framework achieves excellent Quality of Service (QoS) performance under the condition of resource competition, where a shared network with limited bandwidth is optimized.