160 research outputs found
Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform
In this paper, a hierarchical multi-UAV simulation platform,called XTDrone,
is designed for UAV swarms, which is completely open-source 4 . There are six
layers in XTDrone: communication, simulator,low-level control, high-level
control, coordination, and human interac-tion layers. XTDrone has three
advantages. Firstly, the simulation speedcan be adjusted to match the computer
performance, based on the lock-step mode. Thus, the simulations can be
conducted on a work stationor on a personal laptop, for different purposes.
Secondly, a simplifiedsimulator is also developed which enables quick algorithm
designing sothat the approximated behavior of UAV swarms can be observed
inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the
codes in simulations can be easily transplanted to embeddedsystems. Note that
XTDrone can support various types of multi-UAVmissions, and we provide two
important demos in this paper: one is aground-station-based multi-UAV
cooperative search, and the other is adistributed UAV formation flight,
including consensus-based formationcontrol, task assignment, and obstacle
avoidance.Comment: 12 pages, 10 figures. And for the, see
https://gitee.com/robin_shaun/XTDron
Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System
This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour
Stability of Linear Set-Membership Filters With Respect to Initial Conditions: An Observation-Information Perspective
The issue of filter stability with respect to (w.r.t.) the initial condition
refers to the unreliable filtering process caused by improper prior information
of the initial state. This paper focuses on analyzing and resolving the
stability issue w.r.t. the initial condition of the classical Set-Membership
Filters (SMFs) for linear time-invariance systems, which has not yet been well
understood in the literature. To this end, we propose a new concept -- the
Observation-Information Tower (OIT), which describes how the measurements
affect the estimate in a set-intersection manner without relying on the initial
condition. The proposed OIT enables a rigorous stability analysis, a new SMFing
framework, as well as an efficient filtering algorithm. Specifically, based on
the OIT, explicit necessary and sufficient conditions for stability w.r.t. the
initial condition are provided for the classical SMFing framework. Furthermore,
the OIT inspires a stability-guaranteed SMFing framework, which fully handles
the stability issue w.r.t. the initial condition. Finally, with the
OIT-inspired framework, we develop a fast and stable constrained zonotopic SMF,
which significantly overcomes the wrapping effect.Comment: 17 pages, 4 figure
EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model
Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines
Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method
This article studies the distributed estimation problem of a multi-agent
system with bounded absolute and relative range measurements. Parts of the
agents are with high-accuracy absolute measurements, which are considered as
anchors; the other agents utilize lowaccuracy absolute and relative range
measurements, each derives an uncertain range that contains its true state in a
distributed manner. Different from previous studies, we design a distributed
algorithm to handle the range measurements based on extended constrained
zonotopes, which has low computational complexity and high precision. With our
proposed algorithm, agents can derive their uncertain range sequentially along
the chain topology, such that agents with low-accuracy sensors can benefit from
the high-accuracy absolute measurements of anchors and improve the estimation
performance. Simulation results corroborate the effectiveness of our proposed
algorithm and verify our method can significantly improve the estimation
accuracy.Comment: 15 pages 6 figure
Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization
This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-box deep learning models, DF2M offers an explainable approach to utilizing neural networks by constructing a factor model and integrating deep neural networks within the kernel function. Additionally, we develop a computationally efficient variational inference algorithm to infer DF2M. Empirical results from four real-world datasets demonstrate that DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models for high-dimensional functional time series
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