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Al Badr A., Almaghout K.
Abstract
Autonomous navigation in underground mining poses a unique set of challenges, from GPS
unavailability and high dust levels that compromise visual sensors to feature-poor environments
that complicate localization and narrow tunnels that restrict vehicle movement. To address
these issues, this paper presents a novel behavior-based control approach, integrating wallfollowing
for lateral stability as the vehicle progresses toward designated positions. The path to
these targets is generated by an A* algorithm, ensuring efficient route planning within confined
spaces. For localization, an Extended Kalman Filter (EKF) fuses data from wheel odometry
and an Inertial Measurement Unit (IMU), providing robust state estimation in the absence
of GPS. The proposed system leverages a four-wheel steering mechanism with negative-phase
control and is equipped with 3D LiDAR, ultrasonic sensors, wheel encoders, and an IMU for
enhanced situational awareness and control. Simulation results validate the system’s ability to
achieve precise navigation in challenging underground environments, even within tunnels that
allow minimal clearance.
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Alkousa M. S., Stonyakin F. S., Abdo A. M., Alcheikh M. M.
Abstract
This paper is devoted to new mirror descent-type methods with switching between two
types of iteration points (productive and non-productive) for constrained convex optimization
problems with several convex functional (inequality-type) constraints. We propose two methods
(standard one and its modification) with a new weighting scheme for points in each iteration of
methods, which assigns smaller weights to the initial points and larger weights to the most recent
points, thus as a result, it improves the convergence rate of the proposed methods (empirically).
The proposed modification makes it possible to reduce the running time of the method due
to skipping some of the functional constraints at non-productive steps. We derive bounds for
the convergence rate of the proposed methods with time-varying step sizes, which show that
the proposed methods are optimal from the viewpoint of lower oracle estimates. The results of
some numerical experiments, which illustrate the advantages of the proposed methods for some
examples, such as the best approximation problem, the Fermat –Torricelli – Steiner problem, the
smallest covering ball problem, and the maximum of a finite collection of linear functions, are
also presented.
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Bulichev O. V., Maloletov A. V.
Abstract
This paper presents an approach to terrain shape detection using an array of tactile sensors
or motor torque and encoders. A sparse point cloud at points where the surface is touched by the
robot’s feet is converted into a polygonal mesh and a dense 3D point cloud using α-shapes derived
from a 2D Delaunay triangulation. Cloud-to-Cloud (C2C) and Cloud-to-Mesh (C2M) metrics are
used to validate the solution. In the study, a mathematical model of the robot-surface system is
developed and numerical experiments are performed on the basis of this model. A modification
of Delaunay triangulation is proposed to account for impassable or unexplored areas of the
surface. The results of mathematical modeling are confirmed in hardware experiments.
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Bychkov G. K., Dvinskikh D. M., Antsiferova A. V., Gasnikov A. V., Lobanov A. V.
Abstract
We present a novel gradient-free algorithm to solve a convex stochastic optimization problem,
such as those encountered in medicine, physics, and machine learning (e.g., the adversarial
multi-armed bandit problem), where the objective function can only be computed through numerical
simulation, either as the result of a real experiment or as feedback given by the function
evaluations from an adversary. Thus, we suppose that only black-box access to the function
values of the objective is available, possibly corrupted by adversarial noise: deterministic or
stochastic. The noisy setup can arise naturally from modeling randomness within a simulation
or by computer discretization, or when exact values of the function are forbidden due to privacy
issues, or when solving nonconvex problems as convex ones with an inexact function oracle.
By exploiting higher-order smoothness, fulfilled, e.g., in logistic regression, we improve the
performance of zero-order methods developed under the assumption of classical smoothness (or
having a Lipschitz gradient). The proposed algorithm enjoys optimal oracle complexity and is
designed under an overparameterization setup, i.e., when the number of model parameters is
much larger than the size of the training dataset. Overparametrized models fit to the training
data perfectly while also having good generalization and outperforming underparameterized
models on unseen data. We provide convergence guarantees for the proposed algorithm under
both types of noise. Moreover, we estimate the maximum permissible adversarial noise level
that maintains the desired accuracy in the Euclidean setup, and then we extend our results to
a non-Euclidean setup. Our theoretical results are verified using the logistic regression problem.
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Damindarov R. R., Gaponov I., Maloletov A. V.
Abstract
This paper discusses how to develop and implement a bimanual teleoperation system using
an exoskeleton suit and two collaborative robots. In the mathematical model, two methods of
mapping have been implemented: Joint space mapping via direct control and Cartesian space
mapping using Saturation in the Null Space. Both methods are verified in simulation using the
developed mathematical model and on hardware using KUKA IIWA robots. A pick-and-place
experiment is designed, and the corresponding end-effector positions of the master and the slave
devices are obtained. Force feedback is introduced using two methods to improve accuracy and
to show the applicability not only for collaborative robots but also on industrial manipulators.
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Gasnikov A. V., Alkousa M. S., Lobanov A. V., Dorn Y. V., Stonyakin F. S., Kuruzov I. A., Singh S. R.
Abstract
Frequently, when dealing with many machine learning models, optimization problems appear
to be challenging due to a limited understanding of the constructions and characterizations
of the objective functions in these problems. Therefore, major complications arise when dealing
with first-order algorithms, in which gradient computations are challenging or even impossible in
various scenarios. For this reason, we resort to derivative-free methods (zeroth-order methods).
This paper is devoted to an approach to minimizing quasi-convex functions using a recently
proposed (in [56]) comparison oracle only. This oracle compares function values at two points
and tells which is larger, thus by the proposed approach, the comparisons are all we need to solve
the optimization problem under consideration. The proposed algorithm to solve the considered
problem is based on the technique of comparison-based gradient direction estimation and the
comparison-based approximation normalized gradient descent. The normalized gradient descent
algorithm is an adaptation of gradient descent, which updates according to the direction of the
gradients, rather than the gradients themselves. We proved the convergence rate of the proposed
algorithm when the objective function is smooth and strictly quasi-convex in $\mathbb{R}^n$, this algorithm
needs $\mathcal{O}\left( \left(n D^2/\varepsilon^2 \right) \log\left(n D / \varepsilon\right)\right)$ comparison queries to find an $\varepsilon$-approximate of the optimal solution,
where $D$ is an upper bound of the distance between all generated iteration points and an optimal
solution.
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Kirilin A. D., Skvortsova V. A., Koshman V. V.
Abstract
The development of exoskeleton technologies has garnered increasing interest from industrial
companies, particularly in the context of active exoskeletons that are powered by an external
energy source. A key component in these systems is the actuation device, which plays a pivotal
role in their overall performance. Among the various actuation mechanisms, the twisted string
actuator (TSA) has emerged as a promising candidate for wearable robotic systems. The TSA
mimics the behavior of human muscles but offers higher efficiency, making it an attractive
solution for exoskeleton applications.
This study introduces a novel lever-based transmission mechanism designed to adapt TSAs
to human joints that require torque, rather than a linear force. To support this approach, we
developed a mathematical model that accurately describes the dynamics of the proposed mechanism.
A series of experiments were conducted to validate the model, confirming its reliability.
In addition to the theoretical work, we integrated the lever-based TSA into an exoskeleton
and tested its effectiveness in reducing muscle loads. The experiments focused on squatting
exercises, where significant reductions in muscle activity were observed, demonstrating the exoskeleton’s
potential for easing physical strain on users. The accuracy of the lever-based TSA
model was further confirmed by the experiments in predicting the generated force and the actuation
angles achieved. The estimated error between the sensor data and the models predictions
were 1.64 MAE (degrees) for the revolute joint angle and 1.79 MAE (Newtons) for the tension
force of the string.
Moreover, the results showed that the lever-based TSA provided the necessary torques for
the hip joint, aligning well with the natural movement of the human body. This makes it easier
to control and adapt the system in practical exoskeleton applications, enhancing its usability
and effectiveness.
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Klekovkin A. V., Karavaev Y. L., Nazarov A. V.
Abstract
This paper is concerned with the experimental development of the stabilizing regulator for
a spherical pendulum-type robot moving on an oscillating base. Using a mathematical model of
the motion of the spherical robot with an internal pendulum mechanism, a regulator stabilizing
the lower position of the pendulum is developed. The developed regulator has been tested in
practice by means of a real prototype of the spherical robot. The results of real experiments
are presented to assess the stabilization of the lower position of the pendulum of the spherical
robot during its motion along a straight line on a plane executing longitudinal oscillations, and
during the stabilization of the lower position of the pendulum, when the spherical shell remains
fixed relative to the plane.
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Koshman V. V., Skvortsova V. A., Kirilin A. D.
Abstract
The use of exoskeletons in manufacturing, construction, and healthcare shows potential
for enhancing performance and reducing injury risk. This study evaluates exoskeleton efficacy
during heavy lifting using an advanced system integrating electromyography (EMG) and electrocardiography
(ECG). Real-time monitoring with baseline correction and filtering ensured precise
data. EMG analyses using Root Mean Square (RMS) and Integral methods revealed reduced
muscle activation and cumulative exertion during exoskeleton-assisted tasks. ECG data indicated
lower cardiovascular strain. Testing with a hip exoskeleton confirmed its ability to decrease
physical load, emphasizing the value of integrated physiological monitoring for comprehensive
exoskeleton performance assessment and future research directions.
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Krivchenko V. O., Gasnikov A. V., Kovalev D. A.
Abstract
In this paper we present interpolation conditions for several important convex-concave function
classes: nonsmooth convex-concave functions, conditions for difference of strongly-convex
functions in a form that contains oracle information exclusively and smooth convex-concave
functions with a bilinear coupling term. Then we demonstrate how the performance estimation
problem approach can be adapted to analyze the exact worst-case convergence behavior of firstorder
methods applied to composite bilinear-coupled min-max problems. Using the performance
estimation problem approach, we estimate iteration complexities for several first-order fixed-step
methods, Sim-GDA and Alt-GDA, which are applied to smooth convex-concave functions with
a bilinear coupling term.
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Nasybullin A. A., Abdullaev N., Baranov M. A., Koshman V. V., Mahonin V. A.
Abstract
This study presents a methodology for identifying the most informative frequencies and
channels in electromyography (EMG) data to evaluate muscle recovery using Decision Tree classifiers.
EMG signals, recorded from the vastus lateralis muscle during squat exercises, were
analyzed across varying rest intervals to assess optimal recovery periods. By employing single
Decision Tree classifiers, the study enhances interpretability, offering insights into feature importance
— essential for applications in medical and sports settings where transparency is critical.
The experimental protocol utilized a grid search for hyperparameter tuning and cross-validation
to address class imbalance, ultimately achieving a reliable classification of rest intervals based
on power spectral density features. The results indicate that a limited subset of highly informative
features provides sufficient accuracy, suggesting that streamlined, interpretable models
are effective for the evaluation of muscle recovery. This approach can guide future research in
developing compact, robust models adapted to EMG-based diagnostics.
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Nguyen N. T., Rogozin A. V., Gasnikov A. V.
Abstract
The consensus problem in distributed computing involves a network of agents aiming to
compute the average of their initial vectors through local communication, represented by an
undirected graph. This paper focuses on studying this problem using an average-case analysis
approach, particularly over regular graphs. Traditional algorithms for solving the consensus
problem often rely on worst-case performance evaluation scenarios, which may not reflect typical
performance in real-world applications. Instead, we apply average-case analysis, focusing on the
expected spectral distribution of eigenvalues to obtain a more realistic view of performance. Key
contributions include deriving the optimal method for consensus on regular graphs, showing its
relation to the Heavy Ball method, analyzing its asymptotic convergence rate, and comparing
it to various first-order methods through numerical experiments.
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Serenko I. A., Dorn Y. V., Singh S. R., Kornaev A. V.
Abstract
This work addresses uncertainty quantification in machine learning, treating it as a hidden
parameter of the model that estimates variance in training data, thereby enhancing the
interpretability of predictive models. By predicting both the target value and the certainty
of the prediction, combined with deep ensembling to study model uncertainty, the proposed
method aims to increase model accuracy. The approach was applied to the well-known problem
of Remaining Useful Life (RUL) estimation for turbofan jet engines using NASA’s dataset.
The method demonstrated competitive results compared to other commonly used tabular data
processing methods, including k-nearest neighbors, support vector machines, decision trees, and
their ensembles. The proposed method is based on advanced techniques that leverage uncertainty
quantification to improve the reliability and accuracy of RUL predictions.
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Shamin A. Y., Rachkov A. A.
Abstract
This paper is concerned with a mechanical system consisting of a rigid body (outer body)
placed on a horizontal rough plane and of an internal moving mass moving in a circle lying in
a vertical plane, so that the radius vector of the point has a constant angular velocity. The
interaction of the outer body and the horizontal plane is modeled by the Coulomb –Amonton
law of dry friction with anisotropy (the friction coefficient depends on the direction of the body’s
motion). The equation of the body’s motion is a differential equation with a discontinuous
right-hand side. Based on the theory of A. F. Filippov, it is proved that, for this equation, the
existence and right-hand uniqueness of the solution takes place, and that there exists a continuous
dependence on initial conditions. Some general properties of the solutions are established and
possible periodic regimes and their features are considered depending on the parameters of the
problem. In particular, the existence of a periodic regime is proved in the case where the motion
occurs without sticking of the outer body, and conditions for the existence of such a regime are
shown. An analysis is made of the final dynamics of how the system reaches a periodic regime
in the case where the outer body sticks twice within a period of revolution of the internal mass.
This periodic regime exhibits sticking in the so-called upper and lower deceleration zones. The
outer body comes twice to a stop and is at rest in these zones for some time and then continues
its motion. This paper gives a complete description of the solution pattern for such motions.
It is shown that, in the parameter space of the system where such a regime exists, the solution
reaches this regime in finite time. All qualitatively different solutions in this case are described.
In particular, special attention is devoted to the terminal motion, namely, to the solution of
the system during the last period of motion of the internal mass before reaching the periodic
regime. Existence regions of such solutions are found and the boundaries of the regions of initial
conditions determining the qualitatively different dynamics of the system are established.
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Smirnov V. N., Kazistova K. M., Sudakov I. A., Leplat V., Gasnikov A. V., Lobanov A. V.
Abstract
Black-box optimization, a rapidly growing field, faces challenges due to limited knowledge of
the objective function’s internal mechanisms. One promising approach to addressing this is the
Stochastic Order Oracle Concept. This concept, similar to other Order Oracle Concepts, relies
solely on relative comparisons of function values without requiring access to the exact values.
This paper presents a novel, improved estimation of the covariance matrix for the asymptotic
convergence of the Stochastic Order Oracle Concept. Our work surpasses existing research in
this domain by offering a more accurate estimation of asymptotic convergence rate. Finally,
numerical experiments validate our theoretical findings, providing strong empirical support for
our proposed approach.
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Sumenkov O. Y., Kulminskiy D. D., Gusev S. V.
Abstract
This paper presents a practical approach to fully automated kinematic calibration of an industrial
manipulator. The approach is based on the principle of plane constraint. The electrical
signal is used to fix the moment of contact between the conductive tool and the flat surface.
The measurement data are manipulator configurations (joint angles) at the moment of contact.
A modification of the algorithm to deal with the scaling problem is also proposed. This
approach provides both high calibration accuracy and lower cost of the experimental setup compared
to coordinate measuring machines (CMMs), laser trackers, and vision systems. The article
examines the impact of various methods of kinematic parameterization of manipulators: the Denavit
– Hartenberg agreement (DH), product of exponentials (POE), as well as the complete and
parametrically continuous model (CPC) on the calibration accuracy. A comparison is made of
the open-loop and the proposed closed-loop calibration methods on the Puma 560 model known
in the literature. POE parameters were converted to DH and CPC to compare accuracy after
calibration based on these parameterizations. The method of computing POE-CPC transformation
as a solution to a certain optimization problem is proposed. The problem of identifying
geometric parameters in the presence of restrictions is solved by gradient optimization methods.
Experiments have been carried out on an ABB IRB 1600 industrial manipulator with an installed
conductive probe and an ABB IRBP A-500 robotic positioner with a conductive metal flat surface.
A technique for indirectly checking the accuracy of calibration of kinematic parameters
is proposed based on a study of the accuracy of manipulation when using these parameters.
A comparison is made of the manipulation accuracy when using four sets of parameters: nominal
parameters obtained during factory calibration with the Leica AT901B laser tracker and two sets
of parameters obtained by applying the proposed calibration method. The kinematic parameters
obtained from the experiment determine more accurately the position of the manipulator TCP
for part of the configuration working space, even for areas that were not used for calibration.
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