207 research outputs found
Dimension Reduction for Efficient Data-Enabled Predictive Control
The recent data-enabled predictive control (DeePC) paradigm directly utilizes
offline input/output data from an unknown system to predict its future
trajectory and compute optimal control inputs online. In this scheme, the
pre-collected input/output data needs to be sufficiently rich to represent the
system behavior. This generally leads to an excessive amount of offline data,
which consequently results in a high-dimension optimization problem in online
predictive control. In this paper, we propose a simple yet effective singular
value decomposition (SVD) based strategy to reduce the optimization problem
dimension in DeePC. Specifically, in the case of linear time-invariant systems,
the excessive input/output measurements can be rearranged into a smaller data
library for the non-parametric representation of system behavior. Based on this
observation, we use an SVD-based strategy to pre-process the offline data that
achieves dimension reduction in DeePC. Numerical experiments confirm that the
proposed method significantly enhances the computation efficiency without
compromising the control performance.Comment: 9 pages, 4 figure
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Efficiently capturing the complex spatiotemporal representations from
large-scale unlabeled traffic data remains to be a challenging task. In
considering of the dilemma, this work employs the advanced contrastive learning
and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive
Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation
methods for spatiotemporal graph data, which not only perturb the data in terms
of graph structure and temporal characteristics, but also employ a
learning-based dynamic graph view generator for adaptive augmentation. Second,
we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to
simultaneously capture the decent spatial-temporal dependencies and realize
graph-level contrasting. To further discriminate node individuals in negative
filtering, a Semantic Contextual Contrastive method is designed based on
semantic features and spatial heterogeneity, achieving node-level contrastive
learning along with negative filtering. Finally, we present a hard mutual-view
contrastive training scheme and extend the classic contrastive loss to an
integrated objective function, yielding better performance. Extensive
experiments and evaluations demonstrate that building a predictor upon STS-CCL
contrastive learning model gains superior performance than existing traffic
forecasting benchmarks. The proposed STS-CCL is highly suitable for large
datasets with only a few labeled data and other spatiotemporal tasks with data
scarcity issue.Comment: This work was accepted by the 49th IEEE International Conference on
Acoustics, Speech, & Signal Processing (ICASSP 2024). We will present our
work in Seoul, Kore
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
Data-driven cooperative control of connected and automated vehicles (CAVs)
has gained extensive research interest as it can utilize collected data to
generate control actions without relying on parametric system models that are
generally challenging to obtain. Existing methods mainly focused on improving
traffic safety and stability, while less emphasis has been placed on energy
efficiency in the presence of uncertainties and diversities of human-driven
vehicles (HDVs). In this paper, we employ a data-enabled predictive control
(DeePC) scheme to address the eco-driving of mixed traffic flows with diverse
behaviors of human drivers. Specifically, by incorporating the physical
relationship of the studied system and the Hankel matrix update from the
generalized behavior representation to a particular one, we develop a new
Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle
human driver diversities. In particular, a power consumption term is added to
the DeePC cost function to reduce the holistic energy consumption of both CAVs
and HDVs. Simulation results demonstrate the effectiveness of our approach in
accurately capturing random human driver behaviors and addressing the complex
dynamics of mixed traffic flows, while ensuring driving safety and traffic
efficiency. Furthermore, the proposed optimization framework achieves
substantial reductions in energy consumption, i.e., average reductions of 4.83%
and 9.16% when compared to the benchmark algorithms
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Connected and autonomous vehicles (CAVs) promise next-gen transportation
systems with enhanced safety, energy efficiency, and sustainability. One
typical control strategy for CAVs is the so-called cooperative adaptive cruise
control (CACC) where vehicles drive in platoons and cooperate to achieve safe
and efficient transportation. In this study, we formulate CACC as a multi-agent
reinforcement learning (MARL) problem. Diverging from existing MARL methods
that use centralized training and decentralized execution which require not
only a centralized communication mechanism but also dense inter-agent
communication, we propose a fully-decentralized MARL framework for enhanced
efficiency and scalability. In addition, a quantization-based communication
scheme is proposed to reduce the communication overhead without significantly
degrading the control performance. This is achieved by employing randomized
rounding numbers to quantize each piece of communicated information and only
communicating non-zero components after quantization. Extensive experimentation
in two distinct CACC settings reveals that the proposed MARL framework
consistently achieves superior performance over several contemporary benchmarks
in terms of both communication efficiency and control efficacy.Comment: 11 pages, 7 figure
High-Precision Fruit Localization Using Active Laser-Camera Scanning: Robust Laser Line Extraction for 2D-3D Transformation
Recent advancements in deep learning-based approaches have led to remarkable
progress in fruit detection, enabling robust fruit identification in complex
environments. However, much less progress has been made on fruit 3D
localization, which is equally crucial for robotic harvesting. Complex fruit
shape/orientation, fruit clustering, varying lighting conditions, and
occlusions by leaves and branches have greatly restricted existing sensors from
achieving accurate fruit localization in the natural orchard environment. In
this paper, we report on the design of a novel localization technique, called
Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D
localization. The ALACS hardware setup comprises a red line laser, an RGB color
camera, a linear motion slide, and an external RGB-D camera. Leveraging the
principles of dynamic-targeting laser-triangulation, ALACS enables precise
transformation of the projected 2D laser line from the surface of apples to the
3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction
(LLE) method is proposed for robust and high-precision feature extraction on
apples. Comprehensive evaluations of LLE demonstrated its ability to extract
precise patterns under variable lighting and occlusion conditions. The ALACS
system achieved average apple localization accuracies of 6.9 11.2 mm at
distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial
RealSense RGB-D camera, in an indoor experiment. Orchard evaluations
demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71%
rate by the RealSense camera. By overcoming the challenges of apple 3D
localization, this research contributes to the advancement of robotic fruit
harvesting technology
ABatRe-Sim: A Comprehensive Framework for Automated Battery Recycling Simulation
With the rapid surge in the number of on-road Electric Vehicles (EVs), the
amount of spent lithium-ion (Li-ion) batteries is also expected to explosively
grow. The spent battery packs contain valuable metal and materials that should
be recovered, recycled, and reused. However, only less than 5% of the Li-ion
batteries are currently recycled, due to a multitude of challenges in
technology, logistics and regulation. Existing battery recycling is performed
manually, which can pose a series of risks to the human operator as a
consequence of remaining high voltage and chemical hazards. Therefore, there is
a critical need to develop an automated battery recycling system. In this
paper, we present ABatRe-sim, an open-source robotic battery recycling
simulator, to facilitate the research and development in efficient and
effective battery recycling au-omation. Specifically, we develop a detailed CAD
model of the battery pack (with screws, wires, and battery modules), which is
imported into Gazebo to enable robot-object interaction in the robot operating
system (ROS) environment. It also allows the simulation of battery packs of
various aging conditions. Furthermore, perception, planning, and control
algorithms are developed to establish the benchmark to demonstrate the
interface and realize the basic functionalities for further user customization.
Discussions on the utilization and future extensions of the simulator are also
presented
Federated Learning's Blessing: FedAvg has Linear Speedup
Federated learning (FL) learns a model jointly from a set of participating
devices without sharing each other's privately held data. The characteristics
of non-iid data across the network, low device participation, and the mandate
that data remain private bring challenges in understanding the convergence of
FL algorithms, particularly in regards to how convergence scales with the
number of participating devices. In this paper, we focus on Federated Averaging
(FedAvg)--the most widely used and effective FL algorithm in use today--and
provide a comprehensive study of its convergence rate. Although FedAvg has
recently been studied by an emerging line of literature, it remains open as to
how FedAvg's convergence scales with the number of participating devices in the
FL setting--a crucial question whose answer would shed light on the performance
of FedAvg in large FL systems. We fill this gap by establishing convergence
guarantees for FedAvg under three classes of problems: strongly convex smooth,
convex smooth, and overparameterized strongly convex smooth problems. We show
that FedAvg enjoys linear speedup in each case, although with different
convergence rates. For each class, we also characterize the corresponding
convergence rates for the Nesterov accelerated FedAvg algorithm in the FL
setting: to the best of our knowledge, these are the first linear speedup
guarantees for FedAvg when Nesterov acceleration is used. To accelerate FedAvg,
we also design a new momentum-based FL algorithm that further improves the
convergence rate in overparameterized linear regression problems. Empirical
studies of the algorithms in various settings have supported our theoretical
results
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