A team of robots are deployed to accomplish a task while maintaining a viable ad-hoc network capable of supporting data transmissions necessary for task fulfillment. Solving this problem necessitates: 1) estimation of the wireless propagation environment to identify viable point-to-point communication links; 2) determination of end-to-end routes to support data traffic; and 3) motion control algorithms to navigate through spatial configurations that guarantee required minimum levels of service. Therefore, we present methods for: 1) estimation of point-to-point channels using pathloss and spatial Gaussian process models; 2) data routing so as to determine suitable end-to-end communication routes given estimates of point-to-point channel rates; and 3) motion planning to determine robot trajectories restricted to configurations that ensure survival of the communication network. Because of the inherent uncertainty of wireless channels, the model of links and routes is stochastic. The criteria for route selection is to maximize the probability of network survival-defined as the ability to support target communication rates-given achievable rates on local point-to-point links. Maximum survival probability routes for present and future positions are input into a mobility control module that determines robot trajectories restricted to configurations that ensure the probability of network survival stays above a minimum reliability level. Local trajectory planning is proposed for simple environments and global planning is proposed for complex surroundings. The three proposed components are integrated and tested in experiments run in two different environments. Experimental results show successful navigation with continuous end-to-end connectivity.
Robust Control of Mobility and Communications in Autonomous Robot Teams.