Simple Motion Research Articles

Analysis of Samba Bounce in Latin American Dancing

Samba Bounce is analysed in the mechanical point of view. There have been confusion and misunderstanding among the dance teachers as they explain the bounce action designating the height of the body and the force in relation to the music timing. Bounce action has been explained in the syllabus using special terms such as “minimal weight”, “part weight”, “without weight but with pressure” and other terms. These confusions and somewhat ambiguous terms in the scientific sense are clearly resolved by the present mechanical analysis and interpreted with newly introduced concept of “static weight” and “dynamic weight”. In the presentation, the bounce movement is expressed as a simple harmonic vertical motion. The height position, vertical velocity and acceleration of dancer’s body are shown with the forces between the floor and each foot. The difference between dancer’s feeling and observer’s feeling is also explained. figures.

Analysis and design of a macro-mini robotic system for physical human-robot interaction

The macro-mini architecture has been studied in the past to help with physical human-robot interaction. This paper focuses on the investigation of an active macro/active mini robotic system for the manipulation of large payloads with the objective of designing collaborative robotic systems that provide human users with a natural, effective and intuitive interface. More precisely, an analysis of the system is presented with the goal of determining the range of motion necessary for the active mini robot to ensure a favourable user perception, considering several limitations. To this end, the kinematic and dynamic models of the system are described. The different limitations, such as safety standards, kinematic and dynamic capabilities of both robots (macro and mini), human maximum allowable force and others, and their impact on the interaction bandwidth are stated. A simple controller used for simulations is introduced. Finally, simulation results are presented and the phase shift between the macro robot and the mini robot is observed, for a simple harmonic motion of the payload. Along with the phase shift, the mathematical models derived are used to obtain the behaviour of the mini robot's amplitude as a function of the frequency. For the chosen limitations (based on realistic values), it is shown that reducing the payload's maximum allowed velocity by half (from 1ms−1 to0.5ms−1) reduces the necessary motion range of the mini by a factor of approximately 4.

An overset mesh approach for a vibrating cylinder in uniform flow

Abstract This paper has numerically investigated twodimensional laminar flow over a vibrating circular cylinder. Numerical simulation is performed using the dynamic overset mesh method available in commercial software ANSYS FLUENT 19.0. A simple harmonic motion is applied to simulate the cylinder vibration using the user-defined function (UDF) tool in FLUENT. To examine the accuracy and the capability of the present overset mesh approach, two test types of cylinder vibration are simulated: crossflow and inline vibrations. All simulations are performed at a constant Reynolds number (Re = 100) to predict the occurrence of synchronization or lock-in phenomenon. For the case of crossflow vibration, it is observed that lock-in occurs with cylinder oscillation frequency near the Strouhal frequency of the fixed cylinder. However, for the inline vibration, lockin occurs near twice the Strouhal frequency of the fixed cylinder. Furthermore, in the case of crossflow oscillation, the frequency content in the lift coefficients’ time history is successfully linked to the phase portraits’ shape and the vorticity field. The simulation results are consistent with the available published data in the literature. This indicates that the present numerical technique is valid and capable of modeling flows with moving structural systems.

Object Tracking Glove

Hand gestures are a type of nonverbal communication that can be applied to a variety of sectors, including deaf-mute communication, human–computer interaction (HCI), medical applications, robot control and home automation. Various strategies have been used in hand gesture research articles, including those based on instrumented sensor technology and computer vision. In this paper, we have proposed an object tracking glove that uses simple hand motions and sensor technology to operate computer systems. Withthe assistanceof the webcamthe colourofthe object over the glove is detectedand therefore thecolorare going to bereflected onthe PCscreen, this willbe accomplished withthe utilizationof IDE processingreferred to ascalibration. Withan easymovement of the hand, the pointerare oftenmoved tothe acceptablespot onthe PCduring thisapplication the hall sensor acts as a switch or button, which helps to open any menu or software application when the cursor is moved overthe specifiedmenu. Whenever the hall sensor is messed with the magnet then the data from the sensor passes to the microcontroller. This microcontroller sends data to the Bluetooth module, and the signals are then passed on to the system, allowing it to perform the actions. Our application controls the lights/fans of a room controlled using the glove.

Deep Reinforcement Learning Based Adaptation of Pure-Pursuit Path-Tracking Control for Skid-Steered Vehicles

The growing need for autonomous vehicles in the offroad space raises certain complexities that need to be considered more rigorously in comparison to onroad vehicle automation. Popular path control frameworks in onroad autonomy deployments such as the pure-pursuit controller use geometric and kinematic motion models to generate reference trajectories. However in the offroad settings these controllers, despite their merits (low design and computation requirements), could compute dynamically infeasible trajectories as several of the nominal assumptions made by these models don't hold true when operating in a 2.5D terrain. Outside of the notable challenges such as uncertainties and non-linearities/disturbances introduced by the unknown/unmapped 2.5D terrains, additional complexities arise from the use of vehicle architectures such as the skid-steer that experience lateral skidding for achieving simple curvilinear motion. Additionally, linear models of skid-steer vehicles often consist of high modeling uncertainty which renders traditional linear optimal and robust control techniques inadequate given their sensitivity to modeling errors. Nonlinear MPC has emerged as an upgrade, but needs to overcome realtime deployment challenges (including slow sampling time, design complexity, and limited computational resources). This provides an unique opportunity to utilize data-driven adaptive control methods in tailored application spaces to implicitly learn and hence compensate for the unmodeled aspects of the robot operation. In this study, we build an adaptive control framework called Deep Reinforcement Learning based Adaptive Pure Pursuit (DRAPP) where the base structure is that of a geometric Pure-Pursuit (PP) algorithm which is adapted through a policy learned using Deep Reinforcement Learning (DRL). An additional law that enforces a mechanism to account for the rough terrain is added to the DRL policy to prioritize smoother reference-trajectory generation (and thereby more feasible trajectories for lower-level controllers). The adaptive framework converges quickly and generates smoother references relative to a pure 2D-kinematic path tracking controller. This work includes extensive simulations and a bench marking of the DRAPP framework against Nonlinear Model Predictive Control (NMPC) that is an alternate popular choice in literature for this application.

Fast ORB-SLAM Without Keypoint Descriptors.

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 (Mur-Artal and Tardós, 2017) is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2.

Design of a vision-based object tracker for the automated capture of objects by a manipulator

The trajectory of a tennis ball swinging on a pendulum can be estimated by means of object recognition by a stereo camera. Similarly, the motion of the ball can be modelled as a mathematical pendulum with damping. The extended Kalman filter provides the estimated state variables, the deflection angle and the angular velocity of the ball on the pendulum. The prediction of the trajectory of the ball then makes it possible to catch it autonomously at a suitable point by a manipulator. The trajectory for capturing the ball is implemented via a simple motion profile taking into account the temporal and spatial components. Experiments have shown that 85% of all attempts to catch the ball have been successful. Reasons for the unsuccessful attempts were sought and investigated in terms of both temporal and local displacements. Furthermore, improvements and extensions as well as the application of the implementation to other problems of the same methodological class are discussed.

The Effect of Brain Gym on Global Cognitive Function of Institutionalized Older People

Cognitive function of older adults is influenced by several factors such as age, sex, education, and physical activity so that it can impact the decrease in progressive cognitive function. It is necessary to implement cognitive training in old people’s care to prevent or delay the onset of age-related cognitive decline. Brain gym is a series of simple motion exercises and is an alternative therapy that stimulates the brain. This study aims to evaluate the effect of brain gym as a cognitive training intervention on global cognitive functioning of institutionalized older population. The study is a non-randomized quasi-experimental one using pre-and post-test control group design. The participants of the study were 65 years old or older who had been part of gerontological centers from a local community. They were divided into two groups: control and intervention, each one with 15 people. Mini-Mental State Examination was applied to the sample before and after the cognitive training. The brain gym training consists of bi-weekly group meeting for 12 weeks, lasting 50 min each. The treated group had a significantly better global cognitive function. Recalling (memory), orientation and language skills were the domains with the most signifi- cant changes (p < 0.05). On the other hand, no significant changes were found in the participants of control groups during post test. Our results suggest that cognitive training based in brain gym improves global cognitive function of institutionalized older people lease type your abstract here. Abstract of a research paper is typically 200 to 400 words in length, and 150 to 300 words for a review paper. Abstract shall be running continuously (not structured) and shall not include reference citations. Abbreviations that appear only once in the abstract should be defined in full. If abbreviations appear more than once, the full definitions should be provided first before they can be used elsewhere.

Kinematic evaluation of abnormal gait due to spastic paralysis

Abnormal gait patterns in young children with cerebral palsy are not easy to distinguish from gait abnormalities caused by orthopedic diseases and distinctive gait patterns due to individual habits. As a first step to enable early screening of central nervous system diseases only by simple motion measurement, the gait characteristics are evaluated by using the coordination between lower limb joints during walking in this study. A 4-year-old girl who had severe gait disturbance due to hereditary spastic paraplegia, a healthy 2-year-old boy, and five healthy adult male participate in the experiment. During the experiment, kinematic data are collected using an optical motion capture system. The coordination between lower limb joints during one gait cycle is obtained by a singular value decomposition of left and right joint angles, including hip flexion, hip adduction, hip internal rotation, knee flexion, ankle dorsiflexion, ankle inversion, and ankle adduction. By comparing the results of the singular value decomposition, the kinematic characteristics of gait disturbance due to hereditary spastic paraplegia are described.

ЛИНЕЙНАЯ ДИФФЕРЕНЦИАЛЬНАЯ ИГРА УДЕРЖАНИЯ С ПОЛОМКОЙ

A linear simple motion constraint differential game is considered. This game is considered from the part of the first player, who needs to keep the state of the system in a given convex terminal set throughout the game, despite the possible glitch and control of the second player. A glitch is understood as an instantaneous stop of the first player at a previously unknown point in time; after a certain time he will eliminate the glitch and will continue his motion. The player control vectograms are n-dimensional convex compacts that depend on time. To construct a u-stable bridge, the second method of L.S. Pontryagin is used. This is how a multi-valued mapping is constructed on the basis of the alternating integral of L.S. Pontryagin. After that, it is proved that the constructed mapping is a u-stable bridge for the game under consideration if a number of conditions are satisfied. At the end of the article, a simple example on the plane is considered, where the vectors of the players are circles centered at the origin and with a constant radius, while the radius of the circle of the first player is strictly greater than the second. In this example, a u-stable bridge is built according to the method proposed in the article, and an extremal strategy is found for the first player on the constructed u-stable bridge.

Adoption of Multiplayer Online Battle Arena to Introduce Wayang Character

Wayang is considered a cultural heritage that has been passed down from generation to generation in Indonesia. However, the younger generation known as digital natives shows a lack of motivation and interest in Wayang. Therefore, it became an important matter to introduce traditional Wayang to younger audiences in engaging formats. This paper proposes an adaptation of the multiplayer online battle arena or known as MOBA. This type of multiplayer game for the mobile market is gaining high popularity among the young generation because it embraces quick-paced, strategic, and intense battle scenes. We called our game "Wayang Tarung" or "Wayang Battle" in English. Furthermore, the game has several features supporting the narrative of wayang characters, such as simple digital motion comics and character briefing between loading screens. A mechanic-dynamic-aesthetic framework (MDA) is used as a guideline to assure the game has engaging gameplay. Since the game runs asymmetrically, the starting condition for both players must be considered. Therefore, we also apply a game balancing strategy to ensure that each Wayang character has weak and strong points. In addition, we were adapting artificial intelligence from the Clash Royale game to dictate autonomous character behavior. The implementation of the Wayang Tarung game has been evaluated using a heuristic testing method adapted from the norman Nielsen heuristic usability. We also conducted a direct observation in a controlled-lab condition and gathered the data using a pre-post test that evaluated player knowledge of Wayang's character. Based on the excellent mark pre-post test result, we believe that adopting the multiplayer game platform in introducing wayang characters to the young generation has enriched prominent to explore further.

Calculation of the incidence angle modifier of a Linear Fresnel Collector: The proposed declination and zenith angle model compared to the biaxial factored approach

Since their first appearance as a contribution by Professor Francia at the University of Genova, Italy, the Linear Fresnel Collectors (LFC) demonstrated to be an engineering efficient technology for medium to high temperature solar applications. The strength of the LFC concept is related to the simple mirror motion law, to the compactness of the mirror fields (power to land surface ratios), to the lowest resistance to wind, to the system intrinsic scalability. To perform reliable LCOE analyses, robust performance simulation tools are needed. The Authors developed to this aim a 3D ray-tracing model, able to account for shading, blocking, and end effects as a function of LFC geometry, including primary and secondary mirror curvatures. In this paper, a new approach is implemented to reduce huge yearly ray-tracing datasets and provide very compact analytical equations for fast hourly performance simulations. The present model introduces new Incidence Angle Modifier (IAM) correlations based on the declination and zenith angles. The new model demonstrated to fit subhourly 3D ray-tracing data all year long with an overall error lower than 1.5%, well below the best IAM factored models here compared as a general criticism to the biaxial factored approach related to Fresnel applications.

Minimum‐time row transition control of a vision‐guided agricultural robot

AbstractThis paper presents a vision‐based, subspace optimal controller aiming to improve the row transition performance of an agricultural robot in a strawberry field. The contribution of this paper is twofold. First, only RGB cameras, instead of complicated sensor suites, are used for cross‐bed navigation and row alignment. Second, a real‐time adaptive dynamic programming‐based algorithm is designed for an optimal row transition. The conditions for row alignment are derived in an augmented pixel coordinate frame. Based on these conditions, a simple motion rule is utilized to reduce the search space dimension so that the proposed algorithm can be implemented in real‐time. Additionally, the inverse‐dynamics policy of the algorithm is updated using vision feedback at each control step to adapt to uncertainties. The proposed controller is tested in both simulations and field experiments. In a simulation comparison, the minimum‐time solution achieved using the proposed algorithm is 44.7 s, which is very close to that of a benchmark algorithm (44.4 s). However, the CPU time required by the proposed algorithm is only 4.3% of time needed by the benchmark algorithm. Twenty field experiments using the presented design were all successful in row transition, with a mean final alignment error of 0.5 cm.

A deep transferable motion-adaptive fault detection method for industrial robots using a residual–convolutional neural network

Recently, in various industrial fields, including automated manufacturing processes, industrial robots are becoming indispensable equipment; these robots perform repetitive tasks and increase the productivity of the production line with consistent precision and accuracy. Thus, fault diagnostics of industrial robots is an essential strategy to prevent the significant economic losses that can be caused by a sudden stop of a production line due to an industrial robot fault However, previous data-driven industrial robot fault diagnostics are limited because a pre-trained model built for a specific motion may not accurately or consistently detect faults in other motions, due to motion discrepancies. To overcome this difficulty, in this paper, we propose a deep transferable motion-adaptive fault detection method that uses torque ripples for fault detection of industrial robot gearboxes. The proposed method is composed of two stages: (1) a residual–convolutional neural network is used to enhance the performance of feature extraction for simple motions, after first refining raw torque signals by filtering out the motion-dependent signals (2) a binary-supervised domain adaptation is performed to detect faults adaptively on multi-axial motions through adversarial contrastive learning. The efficacy of the proposed method was validated using experimental data from unit-axis and multi-axial welding motions collected from a real industrial robot testbed. The proposed method showed superior fault detection accuracy for the motion adaptation task, as compared to existing methods.

Teaching Physics to Students With Intellectual Disabilities Using Digital Learning Objects

Digital Learning Objects (DLOs) as pedagogical complete structures of learning content contribute to science education. DLOs especially in the form of interactive simulations seem to be promising tools in physics teaching and learning for students with intellectual disabilities (ID). This study used an AB single subject design to evaluate the effects of four DLOs on students’ learning of transverse waves and simple pendulum motion in a special education high school setting. A functional relationship was found between students’ correct responses concerning both scientific terminology and physical phenomena understanding during probe sessions and the DLOs intervention. In addition, a social validity assessment that evaluated students’ attitudes on learning, quality, and engagement, showed that the DLOs helped students with ID to acquire physics content. The students also reported satisfaction from using the DLOs. Suggestions for future research include the design of DLOs especially for students with ID.

The Mechanics of Dance: Using Parametric Equations as Inspiration for Dance Choreography

ABSTRACT Lissajous figures are parametric equations that deconstruct into equations of simple harmonic motion. They were a source of inspiration by artists and mathematicians alike, well before the digital age, due to their esthetic forms and simple equations that could be easily deconstructed. Here for the first time in literature, we present Lissajous pattern analysis in the context of modern dance movement, thereby expanding the physical understanding of dance and redefining the creative choreographic process. Through the implementation of wearable sensors, specifically wireless accelerometers, we have collected movement data from professional dancers to serve as an additional lens to visualize dance in a novel way and to analyze dance mechanics. The resulting Lissajous figures from the movement phrases were used to both inform and inspire creativity in the choreographic process of the Artistic Director of the Bowen McCauley Dance Company to create a new piece of work entitled Lissajous.

Hubungan Antara Variasi Sudut dengan Nilai Periode pada Bandul Menggunakan Mikrokontroller Arduino

Pendulum motion is an example of simple harmonic motion which undergoes back and forth motion through its equilibrium point regularly. In the motion of a pendulum there is a period which is the time it takes to travel one path of the pendulum. In determining the period of the pendulum, there are quantities that affect the period of the pendulum, one of which is the initial deviation. This study aims to prove and determine the relationship between the magnitude of the pendulum deviation with the value of the pendulum period. The value of the period of the pendulum at an angle of &lt;10 0 will be different from the value of the period at an angle of &gt;10 0 . Experiments were carried out using Arduino as a digital practicum tool, this is because the digital practicum tool has a high accuracy value and has a low relative error. The angle variations selected are 10 0 , 20 0 , 30 0 , and 40 0 , the results obtained in the experiment show that there is a significant difference between the coefficient of variation of the pendulum and the coefficient of the pendulum period.

Tip-over prevention system based on motion prediction for teleoperated excavator

Teleoperated hydraulic excavators used in the initial response to disasters have a high risk of tipping over due to communication delays, lack of information and dangerous work sites. We proposed tip-over prediction system based on motion prediction for a teleoperated excavator. The system predicts a future motion of excavator when an operation input is given. This allows the determination of a risk of tip-over by an operation input before an excavator moves. When the risk is detected, the operation input to the actuator is modified to keep the excavator from tip over. Future motion of excavator is predicted by computing in real time the movement of a teleoperated excavator based on a simple motion model of actuators. The proposed method successfully demonstrates tip-over prevention at a steep slope via simulation experiments.

Motion recommendation for online character control

Reinforcement learning (RL) has been proven effective in many scenarios, including environment exploration and motion planning. However, its application in data-driven character control has produced relatively simple motion results compared to recent approaches that have used large complex motion data without RL. In this paper, we provide a real-time motion control method that can generate high-quality and complex motion results from various sets of unstructured data while retaining the advantage of using RL, which is the discovery of optimal behaviors by trial and error. We demonstrate the results for a character achieving different tasks, from simple direction control to complex avoidance of moving obstacles. Our system works equally well on biped/quadruped characters, with motion data ranging from 1 to 48 minutes, without any manual intervention. To achieve this, we exploit a finite set of discrete actions, where each action represents full-body future motion features. We first define a subset of actions that can be selected in each state and store these pieces of information in databases during the preprocessing step. The use of this subset of actions enables the effective learning of control policy even from a large set of motion data. To achieve interactive performance at run-time, we adopt a proposal network and a k-nearest neighbor action sampler.

Analyzing Oscillations on a Bridge

When people cross bridges, they create vibrations on these bridges because of the impulses they exert. In some cases, vertical oscillations have caused bridges to collapse due to a phenomenon called resonance. This paper utilizes simple harmonic motion to analyze the oscillations of bridges to create a mathematical model determining when certain bridges collapse. The paper first utilizes Hooke’s Law and Newton’s Second Law to create a second order differential equation of the motion of the bridge whose solution is a sine wave. Substituting in values for momentum for a singular impulse on the bridge, the paper then solves for the amplitude after the impulse. Adapting this model for impulses at different times and plotting the output graphs using Python, the bridge is shown to exhibit resonance and suggestions for damping are made.