Distributed formation stabilization for mobile agents using virtual tensegrity structures

This paper investigates the distributed formation control problem for a group of mobile Euler-Lagrange agents to achieve global stabilization by using virtual tensegrity structures. Firstly, a systematic approach to design tensegrity frameworks is elaborately explained to confine the interaction relationships between agents, which allows us to obtain globally rigid frameworks. Then, based on virtual tensegrity frameworks, distributed control strategies are developed such that the mobile agents converge to the desired formation globally. The theoretical analysis is further validated through simulations

ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data

Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-specific augmentation techniques. Despite the effectiveness, it is difficult for these methods to scale to practical applications. In this work, we present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks. We first produce augmented samples using the convex combinations of the real samples. Then, we optimize the augmented samples by minimizing the norms of the data scores, i.e., the gradients of the log-density functions. This procedure enforces the augmented samples close to the data manifold. To estimate the scores, we train a deep estimation network with multi-scale score matching. For different image synthesis tasks, we train the score estimation network using different data. We do not require the tuning of the hyperparameters or modifications to the network architecture. The ScoreMix method effectively increases the diversity of data and reduces the overfitting problem. Moreover, it can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ScoreMix method achieve significant improvements

Mapping transient electric fields with picosecond electron bunches

Transient electric fields, which are an important but hardly explored parameter of laser plasmas, can now be diagnosed experimentally with combined ultrafast temporal resolution and field sensitivity, using femtosecond to picosecond electron or proton pulses as probes. However, poor spatial resolution poses great challenges to simultaneously recording both the global and local field features. Here, we present a direct 3D measurement of a transient electric field by time-resolved electron schlieren radiography with simultaneous 80-Êm spatial and 3.7-ps temporal resolutions, analyzed using an Abel inversion algorithm. The electric field here is built up at the front of an aluminum foil irradiated with a femtosecond laser pulse at 1.9 × 1012 W/cm2, where electrons are emitted at a speed of 4 × 106 m/s, resulting in a unique gpeak.valleyh transient electric field map with the field strength up to 105 V/m. Furthermore, time-resolved schlieren radiography with charged particle pulses should enable the mapping of various fast-evolving field structures including those found in plasma-based particle accelerators

Distributed formation tracking using local coordinate systems

This paper studies the formation tracking problem for multi-agent systems, for which a distributed estimator–controller scheme is designed relying only on the agents’ local coordinate systems such that the centroid of the controlled formation tracks a given trajectory. By introducing a gradient descent term into the estimator, the explicit knowledge of the bound of the agents’ speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time. Then, based on the centroid estimation, a distributed control algorithm is proposed to render the formation tracking and stabilization errors to converge to zero, respectively. Finally, numerical simulations are carried to validate our proposed framework for solving the formation tracking problem