A Transient-Goal Driven Communication-Aware Navigation Strategy for Large Human-Populated Environments

Robots deployed in large human-populated indoor environments such as shopping malls, airports etc., inadvertently communicate via wireless networks for enhanced perception and decision making capabilities. Owing to highly dynamic signal attenuation characteristics in such environments, connectivity issues may arise during robotic navigation, leading to disruption in information flow causing potential danger. Exact modeling of signal propagation for estimating spatial signal variation is usually challenging. Moreover, the presence of dynamic humans also add a layer of temporal signal variation complexities. Thus, this paper introduces a generative approach for embedding radio signal strength constraints within networked service/social robot navigation in large human-populated environments. Initially, we propose a Gaussian Process based online spatio-temporal signal strength prediction model that, as opposed to the current state of the art, also aims to take into account the temporal fading arising due to the presence of human crowds. We then devise a transient-goal driven navigation strategy to realize a sub-optimal path towards a goal, that is aimed at resolving both communication-aware and human-aware planning constraints. Evaluations of the proposed signal prediction model demonstrate the advantages of our approach with respect to the current state of the art. The efficacy of the navigation strategy in also demonstrated simulations and using hardware experiments conducted on a robotic wheelchair operating in a large shopping mall.