2,372 research outputs found
A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments
This survey presents a comprehensive review of various methods and algorithms
related to passing-through control of multi-robot systems in cluttered
environments. Numerous studies have investigated this area, and we identify
several avenues for enhancing existing methods. This survey describes some
models of robots and commonly considered control objectives, followed by an
in-depth analysis of four types of algorithms that can be employed for
passing-through control: leader-follower formation control, multi-robot
trajectory planning, control-based methods, and virtual tube planning and
control. Furthermore, we conduct a comparative analysis of these techniques and
provide some subjective and general evaluations.Comment: 18 pages, 19 figure
Distributed Control for a Multi-Agent System to Pass through a Connected Quadrangle Virtual Tube
In order to guide the multi-agent system in a cluttered environment, a
connected quadrangle virtual tube is designed for all agents to keep moving
within it, whose basis is called the single trapezoid virtual tube. There is no
obstacle inside the tube, namely the area inside the tube can be seen as a
safety zone. Then, a distributed swarm controller is proposed for the single
trapezoid virtual tube passing problem. This issue is resolved by a gradient
vector field method with no local minima. Formal analyses and proofs are made
to show that all agents are able to pass the single trapezoid virtual tube.
Finally, a modified controller is put forward for convenience in practical use.
For the connected quadrangle virtual tube, a modified switching logic is
proposed to avoid the deadlock and prevent agents from moving outside the
virtual tube. Finally, the effectiveness of the proposed method is validated by
numerical simulations and real experiments.Comment: 12 pages,14 figures. arXiv admin note: substantial text overlap with
arXiv:2112.0100
Distributed Control within a Trapezoid Virtual Tube Containing Obstacles for UAV Swarm Subject to Speed Constraints
For guiding the UAV swarm to pass through narrow openings, a trapezoid
virtual tube is designed in our previous work. In this paper, we generalize its
application range to the condition that there exist obstacles inside the
trapezoid virtual tube and UAVs have strict speed constraints. First, a
distributed vector field controller is proposed for the trapezoid virtual tube
with no obstacle inside. The relationship between the trapezoid virtual tube
and the speed constraints is also presented. Then, a switching logic for the
obstacle avoidance is put forward. The key point is to divide the trapezoid
virtual tube containing obstacles into several sub trapezoid virtual tubes with
no obstacle inside. Formal analyses and proofs are made to show that all UAVs
are able to pass through the trapezoid virtual tube safely. Besides, the
effectiveness of the proposed method is validated by numerical simulations and
real experiments.Comment: 11 pages, 12 figure
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
The cubic semilocal convergence on two variants of Newton's method
AbstractIn this paper, we discuss two variants of Newton's method without using any second derivative for solving nonlinear equations. By using the majorant function and confirming the majorant sequences, we obtain the cubic semilocal convergence and the error estimation in the Kantorovich-type theorems. The numerical examples are presented to support the usefulness and significance
Efficient Low Rank Matrix Recovery With Flexible Group Sparse Regularization
In this paper, we present a novel approach to the low rank matrix recovery
(LRMR) problem by casting it as a group sparsity problem. Specifically, we
propose a flexible group sparse regularizer (FLGSR) that can group any number
of matrix columns as a unit, whereas existing methods group each column as a
unit. We prove the equivalence between the matrix rank and the FLGSR under some
mild conditions, and show that the LRMR problem with either of them has the
same global minimizers. We also establish the equivalence between the relaxed
and the penalty formulations of the LRMR problem with FLGSR. We then propose an
inexact restarted augmented Lagrangian method, which solves each subproblem by
an extrapolated linearized alternating minimization method. We analyze the
convergence of our method. Remarkably, our method linearizes each group of the
variable separately and uses the information of the previous groups to solve
the current group within the same iteration step. This strategy enables our
algorithm to achieve fast convergence and high performance, which are further
improved by the restart technique. Finally, we conduct numerical experiments on
both grayscale images and high altitude aerial images to confirm the
superiority of the proposed FLGSR and algorithm
Theoretical Studies of Titanium Dioxide for Dye-Sensitized Solar Cell and Photocatalytic Reaction
This chapter aims to provide researchers in the field of photovoltaics with the valuable information and knowledge needed to understand the physics and modeling of titanium dioxide for dye-sensitized solar cell and photocatalytic reaction. The electronic band structure of titanium dioxide, the treatment of the excited state of titanium dioxide, the molecular dynamics and ultrafast quantum dynamics simulations, and several promising photocatalytic schemes and important considerations for theoretical study are addressed and reviewed. The advanced computational strategies and methods and optimized models to achieve exact simulation are described and discussed, including first principle calculations, nonadiabatic molecular and quantum dynamics, wave function propagation methods, and surface construction of titanium dioxide. These advanced theoretical investigations have become highly active areas of photovoltaics research and powerful tools for the supplement and prediction of related experimental efforts
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing
We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications.Comment: 15 page
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