Contributions of Joint Torques, Motion-dependent Term and Gravity to the Generation of Baseball Bat Head Speed

A force capacity evaluation for a given posture may provide better understanding of human motor abilities for applications in sport sciences, rehabilitation and ergonomics. From data on posture and maximum isometric joint torques, the upper-limb force feasible set of the hand was predicted by four models called force ellipsoid, scaled force ellipsoid, force polytope and scaled force polytope, which were compared with a measured force polytope. The volume, shape and force prediction errors were assessed. The scaled ellipsoid underestimated the maximal mean force, and the scaled polytope overestimated it. The scaled force ellipsoid underestimated the volume of the measured force distribution, whereas that of the scaled polytope was not significantly different from the measured distribution but exhibited larger variability. All the models characterized well the elongated shape of the measured force distribution. The angles between the main axes of the modelled ellipsoids and polytopes and that of the measured polytope were compared. The values ranged from 7.3° to 14.3°. Over the entire surface of the force ellipsoid, 39.7% of the points had prediction errors less than 50 N; 33.6% had errors between 50 and 100 N; and 26.8% had errors greater than 100N. For the force polytope, the percentages were 56.2%, 28.3% and 15.4%, respectively.

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Effect of the velocity-dependent potentials on the energy eigenvalues of the Morse potential

Open Physics ◽

10.2478/s11534-012-0018-y ◽

2012 ◽

Vol 10 (4) ◽

Cited By ~ 7

Author(s):

Asim Soylu ◽

Orhan Bayrak ◽

Ismail Boztosun

Keyword(s):

Morse Potential ◽

State Energy ◽

Bound State ◽

Quantum States ◽

Bound State Energy ◽

Energy Eigenvalues ◽

Dependent Term ◽

Oscillator Type ◽

Dependent Potential ◽

Pekeris Approximation

AbstractWe investigate the effect of the isotropic velocity-dependent potentials on the bound state energy eigenvalues of the Morse potential for any quantum states. When the velocity-dependent term is used as a constant parameter, ρ(r) = ρ 0, the energy eigenvalues can be obtained analytically by using the Pekeris approximation. When the velocity-dependent term is considered as an harmonic oscillator type, ρ(r) = ρ 0 r 2, we show how to obtain the energy eigenvalues of the Morse potential without any approximation for any n and ℓ quantum states by using numerical calculations. The calculations have been performed for different energy eigenvalues and different numerical values of ρ 0, in order to show the contribution of the velocity-dependent potential on the energy eigenvalues of the Morse potential.

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Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Frontiers of Mechanical Engineering ◽

10.1007/s11465-020-0623-1 ◽

2021 ◽

Author(s):

Yue Zhao ◽

Feng Gao ◽

Qiao Sun ◽

Yunpeng Yin

Keyword(s):

Legged Robots ◽

Measurement Unit ◽

Support Vector ◽

Hexapod Robot ◽

Terrain Classification ◽

Adaptive Locomotion ◽

Joint Torques ◽

Active Compliance ◽

Outdoor Environments ◽

The Stability

AbstractLegged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

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Optimization of Dynamic Task Location within a Manipulator’s Workspace for the Utilization of the Minimum Required Joint Torques

Electronics ◽

10.3390/electronics10030288 ◽

2021 ◽

Vol 10 (3) ◽

pp. 288

Author(s):

Adam Wolniakowski ◽

Charalampos Valsamos ◽

Kanstantsin Miatliuk ◽

Vassilis Moulianitis ◽

Nikos Aspragathos

Keyword(s):

High Performance ◽

Human Robot Interaction ◽

Optimal Position ◽

End Effector ◽

Joint Torques ◽

Dynamic Task ◽

Arbitrary Position ◽

Simulation Results ◽

Set Up ◽

Task Placement

The determination of the optimal position of a robotic task within a manipulator’s workspace is crucial for the manipulator to achieve high performance regarding selected aspects of its operation. In this paper, a method for determining the optimal task placement for a serial manipulator is presented, so that the required joint torques are minimized. The task considered comprises the exercise of a given force in a given direction along a 3D path followed by the end effector. Given that many such tasks are usually conducted by human workers and as such the utilized trajectories are quite complex to model, a Human Robot Interaction (HRI) approach was chosen to define the task, where the robot is taught the task trajectory by a human operator. Furthermore, the presented method considers the singular free paths of the manipulator’s end-effector motion in the configuration space. Simulation results are utilized to set up a physical execution of the task in the optimal derived position within a UR-3 manipulator’s workspace. For reference the task is also placed at an arbitrary “bad” location in order to validate the simulation results. Experimental results verify that the positioning of the task at the optimal location derived by the presented method allows for the task execution with minimum joint torques as opposed to the arbitrary position.

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Hip joint torques in type II diabetes with and without neuropathy

Hong Kong Physiotherapy Journal ◽

10.1016/j.hkpj.2017.01.004 ◽

2017 ◽

Vol 37 ◽

pp. 27-33 ◽

Cited By ~ 3

Author(s):

Laleh Abadi ◽

Zahra Salahzadeh ◽

Mandana Rezaei ◽

Ali E. Oskouei ◽

Mahmood Reza Azghani

Keyword(s):

Hip Joint ◽

Type Ii Diabetes ◽

Type Ii ◽

Joint Torques

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Joint torque and velocity optimization for a redundant leg of quadruped robot

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417731897 ◽

2017 ◽

Vol 14 (5) ◽

pp. 172988141773189 ◽

Cited By ~ 2

Author(s):

Taihui Zhang ◽

Honglei An ◽

Hongxu Ma

Keyword(s):

Angular Velocity ◽

Sagittal Plane ◽

Load Capacity ◽

Optimization Criterion ◽

Quadruped Robot ◽

Joint Torque ◽

Quadratic Program ◽

Hydraulic Actuator ◽

Joint Torques ◽

Physical Limitations

Hydraulic actuated quadruped robot similar to BigDog has two primary performance requirements, load capacity and walking speed, so that it is necessary to balance joint torque and joint velocity when designing the dimension of single leg and controlling its motion. On the one hand, because there are three joints per leg on sagittal plane, it is necessary to firstly optimize the distribution of torque and angular velocity of every joint on the basis of their different requirements. On the other hand, because the performance of hydraulic actuator is limited, it is significant to keep the joint torque and angular velocity in actuator physical limitations. Therefore, it is essential to balance the joint torque and angular velocity which have negative correlation under the condition of constant power of the hydraulic actuator. The main purpose of this article is to optimize the distribution of joint torques and velocity of a redundant single leg with joint physical limitations. Firstly, a modified optimization criterion combining joint torques with angular velocity that takes both support phase and flight phase into account is proposed, and then the modified optimization criterion is converted into a normal quadratic programming problem. A kind of recurrent neural network is used to solve the quadratic program problem. This method avoids tremendous matrix inversion and fits for time-varying system. The achieved optimized distribution of joint torques and velocity is useful for aiding mechanical design and the following motion control. Simulation results presented in this article confirm the efficiency of this optimization algorithm.

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Physics-Based Seated Posture Prediction for Pregnant Women and Validation Considering Ground and Seat Pan Contacts

Journal of Biomechanical Engineering ◽

10.1115/1.4007006 ◽

2012 ◽

Vol 134 (7) ◽

Cited By ~ 11

Author(s):

Bradley Howard ◽

Aimee Cloutier ◽

Jingzhou (James) Yang

Keyword(s):

Pregnant Women ◽

Objective Function ◽

Linear Regression Analysis ◽

Joint Torque ◽

Joint Torques ◽

Design Variables ◽

Optimization Formulation ◽

Posture Prediction ◽

Seated Posture ◽

End Effectors

An understanding of human seated posture is important across many fields of scientific research. Certain demographics, such as pregnant women, have special postural limitations that need to be considered. Physics-based posture prediction is a tool in which seated postures can be quickly and thoroughly analyzed, as long the predicted postures are realistic. This paper proposes and validates an optimization formulation to predict seated posture for pregnant women considering ground and seat pan contacts. For the optimization formulation, the design variables are joint angles (posture); the cost function is dependent on joint torques. Constraints include joint limits, joint torque limits, the distances from the end-effectors to target points, and self-collision avoidance constraints. Three different joint torque cost functions have been investigated to account for the special postural characteristics of pregnant women and consider the support reaction forces (SRFs) associated with seated posture. Postures are predicted for three different reaching tasks in common reaching directions using each of the objective function formulations. The predicted postures are validated against experimental postures obtained using motion capture. A linear regression analysis was used to evaluate the validity of the predicted postures and was the criteria for comparison between the different objective functions. A 56 degree of freedom model was used for the posture prediction. Use of the objective function minimizing the maximum normalized joint torque provided an R2 value of 0.828, proving superior to either of two alternative functions.

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Are joint torques the Holy Grail of human gait analysis?

Human Movement Science ◽

10.1016/0167-9457(96)00009-7 ◽

1996 ◽

Vol 15 (3) ◽

pp. 423-443 ◽

Cited By ~ 39

Author(s):

Christopher L. Vaughan

Keyword(s):

Gait Analysis ◽

Human Gait ◽

Holy Grail ◽

Joint Torques ◽

Human Gait Analysis

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Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

10.1115/detc2021-69203 ◽

2021 ◽

Author(s):

Ali Nasr ◽

Brokoslaw Laschowski ◽

John McPhee

Keyword(s):

Control Systems ◽

Myoelectric Control ◽

Machine Learning Algorithms ◽

Torque Control ◽

Future Research ◽

Reactive Control ◽

Rhythmic Movements ◽

Myoelectric Signals ◽

Joint Torques ◽

Robotic Leg

Abstract Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

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