48 research outputs found
Safety-Critical Control under Multiple State and Input Constraints and Application to Fixed-Wing UAV
This study presents a framework to guarantee safety for a class of
second-order nonlinear systems under multiple state and input constraints. To
facilitate real-world applications, a safety-critical controller must consider
multiple constraints simultaneously, while being able to impose general forms
of constraints designed for various tasks (e.g., obstacle avoidance). With this
in mind, we first devise a zeroing control barrier function (ZCBF) using a
newly proposed nominal evading maneuver. By designing the nominal evading
maneuver to 1) be continuously differentiable, 2) satisfy input constraints,
and 3) be capable of handling other state constraints, we deduce an ultimate
invariant set, a subset of the safe set that can be rendered forward invariant
with admissible control inputs. Thanks to the development of the ultimate
invariant set, we then propose a safety-critical controller, which is a
computationally tractable one-step model predictive controller (MPC) with
guaranteed recursive feasibility. We validate the proposed framework in
simulation, where a fixed-wing UAV tracks a circular trajectory while
satisfying multiple safety constraints including collision avoidance, bounds on
flight speed and flight path angle, and input constraints.Comment: Accepted for the 2023 62nd IEEE Conference on Decision and Control
(CDC
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
Processing-in-memory (PIM) has been explored for decades by computer
architects, yet it has never seen the light of day in real-world products due
to their high design overheads and lack of a killer application. With the
advent of critical memory-intensive workloads, several commercial PIM
technologies have been introduced to the market ranging from domain-specific
PIM architectures to more general-purpose PIM architectures. In this work, we
deepdive into UPMEM's commercial PIM technology, a general-purpose PIM-enabled
parallel architecture that is highly programmable. Our first key contribution
is the development of a flexible simulation framework for PIM. The simulator we
developed (aka PIMulator) enables the compilation of UPMEM-PIM source codes
into its compiled machine-level instructions, which are subsequently consumed
by our cycle-level performance simulator. Using PIMulator, we demystify UPMEM's
PIM design through a detailed characterization study. Building on top of our
characterization, we conduct a series of case studies to pathfind important
architectural features that we deem will be critical for future PIM
architectures to suppor
Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning
It has previously been shown that the relative reliability of model-based and model-free reinforcement-learning (RL) systems plays a role in the allocation of behavioral control between them. However, the role of task complexity in the arbitration between these two strategies remains largely unknown. Here, using a combination of novel task design, computational modelling, and model-based fMRI analysis, we examined the role of task complexity alongside state-space uncertainty in the arbitration process. Participants tended to increase model-based RL control in response to increasing task complexity. However, they resorted to model-free RL when both uncertainty and task complexity were high, suggesting that these two variables interact during the arbitration process. Computational fMRI revealed that task complexity interacts with neural representations of the reliability of the two systems in the inferior prefrontal cortex
HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatial and Temporal Variations
Place recognition is crucial for robotic localization and loop closure in
simultaneous localization and mapping (SLAM). Recently, LiDARs have gained
popularity due to their robust sensing capability and measurement consistency,
even in the illumination-variant environment, offering an advantage over
traditional imaging sensors. Spinning LiDARs are widely accepted among many
types, while non-repetitive scanning patterns have recently been utilized in
robotic applications. Beyond the range measurements, some LiDARs offer
additional measurements, such as reflectivity, Near Infrared (NIR), and
velocity (e.g., FMCW LiDARs). Despite these advancements, a noticeable dearth
of datasets comprehensively reflects the broad spectrum of LiDAR configurations
optimized for place recognition. To tackle this issue, our paper proposes the
HeLiPR dataset, curated especially for place recognition with heterogeneous
LiDAR systems, embodying spatial-temporal variations. To the best of our
knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset designed
to support inter-LiDAR place recognition with both non-repetitive and spinning
LiDARs, accommodating different field of view (FOV) and varying numbers of
rays. Encompassing the distinct LiDAR configurations, it captures varied
environments ranging from urban cityscapes to high-dynamic freeways over a
month, designed to enhance the adaptability and robustness of place recognition
across diverse scenarios. Notably, the HeLiPR dataset also includes
trajectories that parallel sequences from MulRan, underscoring its utility for
research in heterogeneous LiDAR place recognition and long-term studies. The
dataset is accessible at https: //sites.google.com/view/heliprdataset.Comment: 9 pages, 9 figures, 5 table
A Cache Diagnostics Method
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryDefense Advanced Research Projects Agency (DARPA) / N00039-87-C-0122IBM Corporation / 1249006Semiconductor Research Corporation (SRC) / 87-DP-10
Water-stable MOFs and Hydrophobically Encapsulated MOFs for CO2 Capture from Ambient Air and Wet Flue Gas
The extra CO2 that has already been released into the atmosphere has to be
removed in order to create a world that is carbon neutral. Technologies have
been created to remove carbon dioxide from wet flue gas or even directly from
ambient air, however these technologies are not widely deployed yet. New
generations of creative CO2 capture sorbents have been produced as a
consequence of recent improvements in material assembly and surface chemistry.
We summarize recent progress on water-stable and encapsulated metal-organic
frameworks (MOFs) for CO2 capture under a wide range of environmental and
operating conditions. In particular, newly developed water-stable MOFs and
hydrophobic coating technologies are discussed with insights into their
materials discovery and the synergistic effects between different components of
these hybrid sorbent systems. The future perspectives and directions of
water-stable and encapsulated MOFs are also given for Direct Air Capture of CO2
and CO2 capture from wet flue gas