Consensus learning for distributed coverage control

— A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of sensory information in the environment. A consensus (or flocking) term is introduced in the learning law to allow sharing of parameters among neighbors, greatly increasing learning convergence rates. Convergence and consensus is proven using a Lyapunov-type proof. The controller with parameter consensus is shown to perform better than the basic controller in numerical simulations.

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Mac Schwager, Jean-Jacques E. Slotine, Daniela Rus
• Keywords: Decentralized Controller, ICRA 2008, Optimal Sensing Configuration, Parameter Consensus, Robotics