Decentralized mixing function control strategy for multi-robot informative persistent sensing applications
Gavin Chase Hall · DSpace@MIT (Massachusetts Institute of Technology) · 2014
In this thesis, we present a robust adaptive control law that enables a team of robots to gen-erate locally optimal closed path persistent sensing trajectories through information rich areas of a dynamic, unknown environment. This controller is novel in that it allows the robots to combine their global sensor estimates of the environment using a mixing func-tion to opt for either: (1) minimum variance (probabilistic), (2) Voronoi approximation, or (3) Voronoi (geometric) sensing interpretations and resulting coverage strategies. As the robots travel along their paths, they continuously sample the environment and reshape their paths according to one of these three control strategies so that ultimately, they only travel through regions where sensory information is nonzero. This approach builds on previous work that used a Voronoi-based control strategy to generate coverage paths [32]. Unlike the Voronoi-based coverage controller, the mixing-function-based coverage controller captures the intuition that globally integrated sensor measurements more thoroughly capture infor-mation about an environment than a collection of independent, localized measurements.