Investigation of the motion of an aquatic robot with an internal fast-speed rotor and a nondeformable tail fin

Novel breathing motion model for radiotherapy

Construction of control algorithm in the problem of the planar motion of a friction-powered robot with a flywheel and an eccentric weight

The in-pipe robots are currently of significant interest, considering numerous recent publications on this subject. Such machines can use various locomotion principles: wheeled, tracked (caterpillar), walking (legged), screw-type, worm-type, snake-type, etc. In most cases, such robots are equipped with an active drive system transmitting the torque from a motor shaft to the corresponding locomotion mechanism (wheels, tracks, etc.). The present paper is devoted to the wheeled in-pipe robot that doesn’t need a complex transmission. In such a case, the idea of implementing the vibratory locomotion system driven by an internal unbalanced mass is proposed. The corresponding kinematic diagram of the wheeled vibration-driven in-pipe robot is developed, and the differential equations describing the robot motion are deduced. In order to carry out the virtual experimental investigations, the robot’s simulation model is designed in the SolidWorks software. The major scientific novelty of the present research consists in developing the theoretical foundation for designing and practical implementation of the in-pipe robots driven by the inertial vibration exciters and equipped with the unidirectionally rotating wheels and overrunning clutches. The results of numerical modeling and computer simulation of the robot motion substantiate the possibilities and expediency of implementing the proposed vibration-driven locomotion principles while creating novel designs of the in-pipe robots.

Simultaneous localization and mapping (SLAM) algorithms rely heavily on a good motion model to provide critical information about the robot's current pose. Most of these algorithms assume that the distribution defining a robot's motion will remain stationary over the period of operation, and as such use a fixed model for the duration of a trial. This does not easily allow for changes in the robot's motion model due to surface changes, wear and tear, and battery life. Also, if new robots of a similar class are to be used, a new motion model may need to be constructed from scratch. In this paper, we introduce a method that allows the robot to automatically learn its motion model, given a rough estimate of its model or the model from a robot of similar class. We validate our method by demonstrating that it learns a new motion model when a robot crosses a threshold onto a different surface. We also demonstrate our method can estimate the motion model for a new robot given the motion model of a robot of similar class.

Describes a prototype and analytical studies of a spherical rolling robot, a new design of a nonholonomic robot system. The spherical robot is driven by two remotely controlled, internally mounted rotors that induce the ball to roll and spin on a flat surface. It is tracked on the plane by an overhead camera. A mathematical model of the robot's motion was developed using the nonholonomic constraints on its motion. For a number of simple motions, it is shown experimentally that the model agrees well with the results. Methods were developed for planning feasible, minimum time and minimum energy trajectories for the robot. These methods are illustrated both by mathematical simulation and hardware experiments.

Motion of a rigid body with cavities filled with viscous fluid at small Reynolds numbers

The paper discusses the dynamic behavior of a double mathematical pendulum with identical parameters of links and end loads, which is under the influence of viscous friction in both of its joints with generally different dissipative coefficients. A linear mathematical model of the system motion for small deviations is given, and a characteristic equation containing two dimensionless dissipative parameters is derived. For the case of low damping, approximate analytical expressions are found that make it possible to evaluate and compare with each other the damping factors during the motion of the system on each of the oscillation modes. A diagram of dissipative motion regimes is constructed, which arises when the plane of dimensionless parameters is divided by discriminant curves into regions with a qualitatively different character of the system motion. It is noted that a dissipative internal resonance can take place in the system under consideration, and the conditions for its existence in an analytical form are established, as well as their graphic illustration is also given. This article is the first part of the study of the dynamics of a dissipative double pendulum, the continuation of which will be presented as a separate work “Dynamics of a double pendulum with viscous friction in the joints. II. Dissipative oscillation modes and optimization of damping parameters”.

This paper is concerned with the motion of an aquatic robot whose body has the form of a sharp-edged foil. The robot is propelled by rotating the internal rotor without shell deformation. The motion of the robot is described by a finite-dimensional mathematical model derived from physical considerations. This model takes into account the effect of added masses and viscous friction. The parameters of the model are calculated from comparison of experimental data and numerical solution to the equations of rigid body motion and the Navier–Stokes equations. The proposed mathematical model is used to define controls implementing straight-line motion, motion in a circle, and motion along a complex trajectory. Experiments for estimation of the efficiency of the model have been conducted.

An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the dynamic Gaussian mixture model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot.

This chapter is focused on creation, accuracy, and simulation of two-parameter control of a mathematical model of motion of aircraft in a flying simulator. We are discussing many of the important advances in applied aircraft modeling. Modeling on various computer architectures (central, distributed, parallel) has an impact on a structure of a mathematical model of aircraft. An important part is the way of description of a numerical method and its accuracy, use of distributed memory system, and shared memory system are presented in the chapter. Motivation of this research is implementation of the general-purpose message passing interface and graphics processing units as inexpensive arithmetic-processing units bring a relevant amount of computing power to desktop personal computers. The chapter is focused on exploitation of parallel techniques of simulation features, computation time of parallel methods of implementation, and improved simulation of a continuous mathematical model of aircraft motion in a flying simulator. The use and application of modeling methods and parallel simulation techniques determine the structure of the mathematical model used in the flying simulator. The effectiveness of our solution is confirmed by providing simulation results obtained by two-parameter control of the mathematical model of aircraft motion.

In the recent past the design of many aquatic robots has been inspired by the motion of fish. Actuated internal rotors or moving masses have been frequently used either for propulsion and or the control of such robots. However the effect of internal passive degrees of freedom or passive appendages on the motion of such robots is poorly understood. In this paper we present a minimal model that demonstrates the influence of passive degrees of freedom on an aquatic robot. The model is of a circular cylinder with a passive internal rotor, immersed in an inviscid fluid interacting with point vortices. We show through numerics that the motion of the cylinder containing a passive degree of freedom is significantly different than one without. These results show that the mechanical feedback via passive degrees of freedom could be a useful way to control the motion of aquatic robots.

Machine learning methods are often applied to the problem of learning a map from a robot's sensor data, but they are rarely applied to the problem of learning a robot's motion model. The motion model, which can be influenced by robot idiosyncrasies and terrain properties, is a crucial aspect of current algorithms for Simultaneous Localization and Mapping (SLAM). In this paper we concentrate on generating the correct motion model for a robot by applying EM methods in conjunction with a current SLAM algorithm. In contrast to previous calibration approaches, we not only estimate the mean of the motion, but also the interdependencies between motion terms, and the variances in these terms. This can be used to provide a more focused proposal distribution to a particle filter used in a SLAM algorithm, which can reduce the resources needed for localization while decreasing the chance of losing track of the robot's position. We validate this approach by recovering a good motion model despite initialization with a poor one. Further experiments validate the generality of the learned model in similar circumstances.

The neural network is applied for correction of the mathematical model of vessel motion. The data obtained during the model tests in the standard maneuver mode "zigzag 20/20" have been used for its training. The data set training the neural network has been obtained by means of random variations with normal distribution of the initially calculated parameters of the model. During the computer tests of the varied model, the measurable kinematic parameters for the characteristic moments of maneuvering have been recorded. These are the moments of the beginning of the rudder throwing from side to side and the moments of the subsequent maximum yawing of the vessel. For six such moments, seven parameters are saved: time, linear speed, angular rate of turn, course and coordinates of the vessel (42 input data for network training). In the Statistica Neural Nets (SNN) software environment, the network has been trained on the basis of 600 sets of such data using the IPS intelligent problem solver built into the SNN environment. The listed data are the network input, and the output ones are the parameters of the mathematical model. The network trained in this way allows for the given maneuvering characteristics, for example, determined by full-scale tests, to find a set of model parameters. If it is necessary to correct the model to meet the changed maneuvering requirements, using them as input to the already trained network, at the output we will obtain a set of model parameters adequate to these changed requirements. The most complex mathematical model in movements is considered, which is expanded to 19 parameters by additionally including two coefficients of added masses and the added moment of inertia of the vessel. All this makes it possible to obtain refined parameters of the mathematical model of the vessel's motion as output variables of the network. The analysis of the results allows us to draw a number of conclusions about the applicability of this approach and the degree of its effectiveness.

In this paper it is fulfilled the analysis of the mathematical model of GLONASS satellite motion. The gravitation pole of the Earth, the gravitation attraction of the Sun, Moon and other planets of the Sun system, solar radiation and solar-moon tides are taken into account in the mathematical model of GLONASS spacecraft motion. The creation of equivalent solar radiation model is the main fundamental problem now. We suggest to solve this problem with the help of unique methodics and special more detail information about parameters of satellite motion. We also propoused the different means of improvement of the gravitation forces modeling. The high precision mathematical model of GLONASS satellite motion which will create with the help of such methodics of forces modeling allow us to solve different scientific and technical problems.

The mechanisms underlying the coordinated beating of cilia and flagella remain incompletely understood despite the fundamental importance of these organelles. The axoneme (the cytoskeletal structure of cilia and flagella) consists of microtubule doublets connected by passive and active elements. The motor protein dynein is known to drive active bending, but dynein activity must be regulated to generate oscillatory, propulsive waveforms. Mathematical models of flagellar motion generate quantitative predictions that can be analysed to test hypotheses concerning dynein regulation. One approach has been to seek periodic solutions to the linearized equations of motion. However, models may simultaneously exhibit both periodic and unstable modes. Here, we investigate the emergence and coexistence of unstable and periodic modes in three mathematical models of flagellar motion, each based on a different dynein regulation hypothesis: (i) sliding control; (ii) curvature control and (iii) control by interdoublet separation (the 'geometric clutch' (GC)). The unstable modes predicted by each model are used to critically evaluate the underlying hypothesis. In particular, models of flagella with 'sliding-controlled' dynein activity admit unstable modes with non-propulsive, retrograde (tip-to-base) propagation, sometimes at the same parameter values that lead to periodic, propulsive modes. In the presence of these retrograde unstable modes, stable or periodic modes have little influence. In contrast, unstable modes of the GC model exhibit switching at the base and propulsive base-to-tip propagation.