229 research outputs found
On stabilizability and exact observability of stochastic systems with their applications
This paper discusses the stabilizability, weak stabilizability, exact
observability and robust quadratic stabilizability of linear stochastic control
systems. By means of the spectrum technique of the generalized Lyapunov
operator, a necessary and sufficient condition is given for stabilizability and
weak stabilizability of stochastic systems, respectively. Some new concepts
called unremovable spectrums, strong solutions, and weakly feedback stabilizing
solutions are introduced. An unremovable spectrum theorem is given, which
generalizes the corresponding theorem of deterministic systems to stochastic
systems. A stochastic Popov-Belevith-Hautus (PBH) criterion for exact
observability is obtained. For applications, we give a comparison theorem for
generalized algebraic Riccati equations (GAREs), and two results on
Lyapunov-type equations are obtained, which improve the previous works.
Finally, we also discuss robust quadratic stabilization of uncertain stochastic
systems, and a necessary and sufficient condition is given for quadratic
stabilization via a linear matrix inequality (LMI)
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
Intensity Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging
It is challenging to stitch multiple images with different exposures due to
possible color distortion and loss of details in the brightest and darkest
regions of input images. In this paper, a novel intensity mapping algorithm is
first proposed by introducing a new concept of weighted histogram averaging
(WHA). The proposed WHA algorithm leverages the correspondence between the
histogram bins of two images which are built up by using the non-decreasing
property of the intensity mapping functions (IMFs). The WHA algorithm is then
adopted to synthesize a set of differently exposed panorama images. The
intermediate panorama images are finally fused via a state-of-the-art
multi-scale exposure fusion (MEF) algorithm to produce the final panorama
image. Extensive experiments indicate that the proposed WHA algorithm
significantly surpasses the related state-of-the-art intensity mapping methods.
The proposed high dynamic range (HDR) stitching algorithm also preserves
details in the brightest and darkest regions of the input images well. The
related materials will be publicly accessible at
https://github.com/yilun-xu/WHA for reproducible research.Comment: 11 pages, 5 figure
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
Over the past few years, monocular depth estimation and completion have been
paid more and more attention from the computer vision community because of
their widespread applications. In this paper, we introduce novel physics
(geometry)-driven deep learning frameworks for these two tasks by assuming that
3D scenes are constituted with piece-wise planes. Instead of directly
estimating the depth map or completing the sparse depth map, we propose to
estimate the surface normal and plane-to-origin distance maps or complete the
sparse surface normal and distance maps as intermediate outputs. To this end,
we develop a normal-distance head that outputs pixel-level surface normal and
distance. Meanwhile, the surface normal and distance maps are regularized by a
developed plane-aware consistency constraint, which are then transformed into
depth maps. Furthermore, we integrate an additional depth head to strengthen
the robustness of the proposed frameworks. Extensive experiments on the
NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds
in performance prior state-of-the-art monocular depth estimation and completion
competitors. The source code will be available at
https://github.com/ShuweiShao/NDDepth.Comment: Extension of previous work arXiv:2309.1059
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