Abstract-A decentralized, adaptive control law is presented to drive a network of mobile robots to a near-optimal sensing configuration. The control law is adaptive in that it integrates sensor measurements to provide a converging estimate of the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. A Lyapunov-type proof is used to show that the control law causes the network to converge to a near-optimal sensing configuration, and the controller is demonstrated in numerical simulations. This technique suggests a broader application of adaptive control methodologies to decentralized control problems in unknown dynamical environments.

Abstract-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.

Abstract-This article presents a simple synchronization framework that can be directly applied to cooperative control of multi-agent systems and oscillation synchronization in robotic manipulation and teleoperation. A dynamical network of multiple Lagrangian systems is constructed by adding diffusive couplings to otherwise freely moving or flying robots. The proposed decentralized tracking control law synchronizes an arbitrary number of robots into a common trajectory with global exponential convergence. The proposed strategy is much simpler than earlier work in terms of both the computational load and the required signals. Furthermore, in contrast with prior work which used simple double integrator models, the proposed method permits highly nonlinear systems and is further extended to time-delayed communications, adaptive control, partial-joint coupling, and leader-follower networks.

We investigate a biologically motivated approach to fast visual classification, directly inspired by the recent work [13]. Specifically, trading-off biological accuracy for computational efficiency, we explore using standard wavelet transforms and patch transforms to parallel the tuning of visual cortex V1 and V4 cells, alternated with max operations to achieve scale and translation invariance. A feature selection procedure is applied during learning to accelerate recognition. We introduce a simple attention-like feedback mechanism, significantly improving recognition and robustness in multiple-object scenes. In experiments, the proposed algorithm achieves or exceeds state-of-the-art performance in object recognition, but also in new applications such as texture classification, satellite image classification, and language identification. Preliminary results on sound classification are shown as well.