Adaptive self-management of teams of autonomous vehicles

Unmanned Autonomous Vehicles (UAVs) are increasingly deployed for missions that are deemed dangerous or impractical to perform by humans in many military and disaster scenarios. Collaborating UAVs in a team form a Self- Managed Cell (SMC) with at least one commander. UAVs in an SMC may need to operate independently or in sub- groups, out of contact with the commander and the rest of the team in order to perform specific tasks, but must still be able to eventually synchronise state information. The SMC must also cope with intermittent and permanent communication failures as well permanent UAV failures. This paper describes a failure management scheme that copes with both communication link and UAV failures, which may result in temporary disjoint sub-networks within the SMC. A communication management protocol is proposed to control UAVs performing disconnected individual operations, while maintaining the SMCs structure by trying to ensure that all members of the mission regardless of destination or task, can communicate by moving UAVs to act as relays or by allowing the UAVs to rendezvous at intermittent intervals. Copyright 2008 ACM.Accepted versio

Bayesian Gait Optimization for Bipedal Locomotion

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization. © 2014 Springer International Publishing

Effect of the Intrinsic Width on the Piezoelectric Force Microscopy of a Single Ferroelectric Domain Wall

Intrinsic domain wall width is a fundamental parameter that reflects bulk ferroelectric properties and governs the performance of ferroelectric memory devices. We present closed-form analytical expressions for vertical and lateral piezoelectric force microscopy (PFM) profiles for the conical and disc models of the tip, beyond point charge and sphere approximations. The analysis takes into account the finite intrinsic width of the domain wall, and dielectric anisotropy of the material. These analytical expressions provide insight into the mechanisms of PFM image formation and can be used for quantitative analysis of the PFM domain wall profiles. PFM profile of a realistic domain wall is shown to be the convolution of its intrinsic profile and resolution function of PFM.Comment: 25 pages, 5 figures, 3 tables, 3 Appendices, To be submitted to J. Appl. Phy

Feedback Error Learning for Rhythmic Motor Primitives

Abstract — Rhythmic motor primitives can be used to learn a variety of oscillatory behaviors from demonstrations or reward signals, e.g., hopping, walking, running and ball-bouncing. However, frequently, such rhythmic motor primitives lead to failures unless a stabilizing controller ensures their functionality, e.g., a balance controller for a walking gait. As an ideal oscillatory behavior requires the stabilizing controller only for exceptions, e.g., to prevent failures, we devise an online learning approach that reduces the dependence on the stabilizing controller. Inspired by related approaches in model learning, we employ the stabilizing controller’s output as a feedback error learning signal for adapting the gait. We demonstrate the resulting approach in two scenarios: a rhythmic arm’s movements and gait adaptation of an underactuated biped. I

Modeling of micro- and nano-scale domain recording by high-voltage atomic force microscopy in ferroelectrics-semiconductors

The equilibrium sizes of micro- and nano-domains caused by electric field of atomic force microscope tip in ferroelectric semiconductor crystals have been calculated. The domain was considered as a prolate semi-ellipsoid with rather thin domain walls. For the first time we modified the Landauer model allowing for semiconductor properties of the sample and the surface energy of the domain butt. The free carriers inside the crystal lead to the formation of the screening layer around the domain, which partially shields its interior from the depolarization field. We expressed the radius and length of the domain though the crystal material parameters (screening radius, spontaneous polarization value, dielectric permittivity tensor) and atomic force microscope tip characteristics (charge, radius of curvature). The obtained dependence of domain radius via applied voltage is in a good quantitative agreement with the ones of submicron ferroelectric domains recorded by high-voltage atomic force and scanning probe microscopy in LiNbO3 and LiTaO3 crystals.Comment: 21 pages, 5 figure