1,721,086 research outputs found
High-level environment representations for mobile robots
Full text linkIn most robotic applications we are faced with the problem of building
a digital representation of the environment that allows the robot to
autonomously complete its tasks. This internal representation can be
used by the robot to plan a motion trajectory for its mobile base
and/or end-effector. For most man-made environments we do not have
a digital representation or it is inaccurate. Thus, the robot must
have the capability of building it autonomously. This is done by
integrating into an internal data structure incoming sensor
measurements. For this purpose, a common solution consists in solving
the Simultaneous Localization and Mapping (SLAM) problem. The map
obtained by solving a SLAM problem is called ``metric'' and it
describes the geometric structure of the environment. A metric map is
typically made up of low-level primitives (like points or
voxels). This means that even though it represents the shape of the
objects in the robot workspace it lacks the information of which
object a surface belongs to. Having an object-level representation of
the environment has the advantage of augmenting the set of possible
tasks that a robot may accomplish. To this end, in this thesis we
focus on two aspects. We propose a formalism to represent in a uniform
manner 3D scenes consisting of different geometric primitives,
including points, lines and planes. Consequently, we derive a local
registration and a global optimization algorithm that can exploit this
representation for robust estimation. Furthermore, we present a
Semantic Mapping system capable of building an \textit{object-based}
map that can be used for complex task planning and execution. Our
system exploits effective reconstruction and recognition techniques
that require no a-priori information about the environment and can be
used under general conditions
Modeling of set/reset operations in NiO-based resistive-switching memory (RRAM) devices
No full textResistive-switching memory (RRAM) devices are attracting a considerable interest in view of their back-end integration, fast programming, and high scalability. Prediction of the programming voltages and currents as a function of the operating conditions is an essential task for developing compact and numerical models able to handle a large number (106–109) of cells within an array. Based on recent experimental findings on the set and reset processes, we have developed physics-based analytical models for the set and reset operations in NiO-based RRAMs. Simulation results obtained by the analytical models were compared with experimental data for variable pulse conditions and were found consistent with data. The set transition is described by a threshold switching process at the broken conductive filament (CF), while the reset transition is viewed as a thermally driven dissolution and/or oxidation of the CF. Set and resetmodels are finally used for reliability predictions of failure times under constant voltage stress (read disturb) and elevated-temperature bake (data retention)
Resistance-dependent switching in NiO-based filamentary RRAM devices
No full textResistive switching memory (RRAM) attracts a growing
research interest as potential high-density Flash replacement.
Although several transition metal oxides have shown resistance
switching capabilities, NiO is considered one of the most promising
materials due to its stable and unipolar switching [1]. However, the
scalability and variability of the programming currents and voltages are still a matter of concern. This work provides a comprehensive understanding of set/reset properties of NiO-based RRAM. The relationship between active-filament size and programming current/voltage is studied by physical modeling of thermally-driven reset and field-driven set transitions [2]. The developed models may serve as numerical/analytical design tools for future RRAM device
Size-Dependent Drift of Resistance Due to Surface Defect Relaxation in Conductive-Bridge Memory
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