Human Box Delivering Simulation by Subtask Division

2018 ◽  
Author(s):  
Paul Owens ◽  
Yujiang Xiang
Keyword(s):  
Boundary Conditions ◽  
Objective Function ◽  
Optimization Problem ◽  
Joint Angle ◽  
Joint Torque ◽  
Reverse Process ◽  
Design Variables ◽  
Simulation Results

This study addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, initial transition step, carrying, final transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying tasks, the objective function is the dynamic effort. In contrast, for transition task, the objective function is the combination of dynamic effort and joint angle discomfort. For unloading, it is a reverse process of lifting motion. A viable optimization motion is generated from the simulation results. The joint torque, joint angle, and ground reactive forces are analyzed for the delivering motion which is also empirically validated.

Shape Optimization of Hydraulic Structures: an Example of an Optimum Design of a Fish Passage

10.29007/2k64 ◽  
2018 ◽  
Author(s):  
Pat Prodanovic ◽  
Cedric Goeury ◽  
Fabrice Zaoui ◽  
Riadh Ata ◽  
Jacques Fontaine ◽  
...  
Keyword(s):  
Numerical Model ◽  
Shape Optimization ◽  
Objective Function ◽  
Optimum Design ◽  
Optimization Problem ◽  
A Priori ◽  
Coupled System ◽  
Fish Passage ◽  
Hydraulic Structures ◽  
Design Variables

This paper presents a practical methodology developed for shape optimization studies of hydraulic structures using environmental numerical modelling codes. The methodology starts by defining the optimization problem and identifying relevant problem constraints. Design variables in shape optimization studies are configuration of structures (such as length or spacing of groins, orientation and layout of breakwaters, etc.) whose optimal orientation is not known a priori. The optimization problem is solved numerically by coupling an optimization algorithm to a numerical model. The coupled system is able to define, test and evaluate a multitude of new shapes, which are internally generated and then simulated using a numerical model. The developed methodology is tested using an example of an optimum design of a fish passage, where the design variables are the length and the position of slots. In this paper an objective function is defined where a target is specified and the numerical optimizer is asked to retrieve the target solution. Such a definition of the objective function is used to validate the developed tool chain. This work uses the numerical model TELEMAC- 2Dfrom the TELEMAC-MASCARET suite of numerical solvers for the solution of shallow water equations, coupled with various numerical optimization algorithms available in the literature.

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

2012 ◽  
Vol 134 (7) ◽  
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.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Design Human-Robot Collaborative Lifting Task Using Optimization

2021 ◽  
Author(s):  
Asif Arefeen ◽  
Yujiang Xiang
Keyword(s):  
Degrees Of Freedom ◽  
Joint Angle ◽  
Joint Torque ◽  
Robotic Arm ◽  
Inverse Dynamic ◽  
Floating Base ◽  
Grasping Forces ◽  
Design Variables ◽  
Human Arm ◽  
Lifting Task

Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.

scholarly journals Single Task Optimization-Based Planar Box Delivery Motion Simulation and Experimental Validation

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Yujiang Xiang ◽  
Shadman Tahmid ◽  
Paul Owens ◽  
James Yang
Keyword(s):  
Joint Angle ◽  
Inverse Dynamics ◽  
Optimization Method ◽  
Joint Torque ◽  
Motion Simulation ◽  
Single Task ◽  
Joint Torques ◽  
Time Integral ◽  
Design Variables ◽  
Complicated Task

Abstract Box delivery is a complicated task and it is challenging to predict the box delivery motion associated with the box weight, delivering speed, and location. This paper presents a single task-based inverse dynamics optimization method for determining the planar symmetric optimal box delivery motion (multi-task jobs). The design variables are cubic B-spline control points of joint angle profiles. The objective function is dynamic effort, i.e., the time integral of the square of all normalized joint torques. The optimization problem includes various constraints. Joint angle profiles are validated through experimental results using root-mean-square-error (RMSE) and Pearson’s correlation coefficient. This research provides a practical guidance to prevent injury risks in joint torque space for workers who lift and deliver heavy objects in their daily jobs.

scholarly journals Static Balancing of Robot Mechanisms and Manipulation Devices

2018 ◽  
Vol 68 (2) ◽  
pp. 77-90
Author(s):  
Stefan Segla
Keyword(s):  
Objective Function ◽  
Multibody Dynamics ◽  
Optimization Problem ◽  
Energy Savings ◽  
Driving Forces ◽  
Optimal Designs ◽  
Constant Force ◽  
Static Balancing ◽  
Design Variables ◽  
Gravity Load

AbstractThe paper deals with static balancing of various kinds of mechanisms and manipulation devices using spring balancing mechanisms. In case of parallelogram robots and manipulation mechanisms a spring balancing mechanism exerting a constant force is used. Problems of static balancing of variable payloads are also presented and investigated in the paper. Static balancing is formulated as an optimization problem with the objective function expressing minimization of the forces acting in the driving joints. As design variables geometrical variables and spring stiffnesses and their unloaded lengths are used. Optimization Toolbox for Use with Matlab and GOOD (Generator Of Optimal Designs) are used to solve the static balancing problems. The optimized mechanisms are evaluated by using multibody dynamics programs taking into account friction effects in mechanism joints. The results of static balancing optimization show essential reduction of the gravity load in drive joints and consequently driving forces with important energy savings.

scholarly journals PENENTUAN KRITERIA PENGHENTIAN ITERASI PADA ALGORITMA STROBERI

2018 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Mutia Nur Estri ◽  
Siti Rahmah Nurshiami ◽  
Rina Reorita ◽  
Muhammad Okky Ibrohim
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Quadratic Function ◽  
Stopping Criterion ◽  
Stopping Criteria ◽  
Simulation Results ◽  
Iterative Error

This paper discusses the application of two types of stopping criterion on the strawberry algorithm, which are stopping criteria based on iterative error and Cauchy criterion. Furthermore, the strawberry algorithm program is simulated on the optimization problem with the objective function is quadratic function. The simulation results on optimization problem with the objective function is quadratic function show that strawberry algorithm with stopping criterion based on Cauchy criterion has the best performance, when compared with stopping criterion based on iterative error and without stopping criterion

scholarly journals Optimal Constrained Layer Damping of Beams: Experimental and Numerical Studies

1995 ◽  
Vol 2 (6) ◽  
pp. 445-450 ◽  
Author(s):  
J.-L. Marcelin ◽  
S. Shakhesi ◽  
F. Pourroy
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Problem ◽  
Damping Factor ◽  
Constrained Layer Damping ◽  
Numerical Studies ◽  
Design Variables ◽  
Optimal Damping ◽  
Viscoelastic Layers

This article deals with the optimal damping of beams constrained by viscoelastic layers when only one or several portions of the beam are covered. The design variables are the dimensions and locations of the viscoelastic layers and the objective function is the maximum damping factor. The discrete design variable optimization problem is solved using a genetic algorithm. Numerical results for minimum and maximum damping are compared to experimental results. This is done for a various number of materials and beams.

An inverse optimization approach for determining weights of joint displacement objective function for upper body kinematic posture prediction

Robotica ◽  
2011 ◽  
Vol 30 (3) ◽  
pp. 389-404 ◽  
Author(s):  
Qiuling Zou ◽  
Qinghong Zhang ◽  
Jingzhou (James) Yang ◽  
Jared Gragg
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Inverse Optimization ◽  
Displacement Function ◽  
Upper Body ◽  
Optimization Approach ◽  
Joint Angles ◽  
Design Variables ◽  
Posture Prediction ◽  
Joint Displacement

SUMMARYHuman posture prediction can often be formulated as a nonlinear multiobjective optimization (MOO) problem. The joint displacement function is considered as a benchmark of human performance measures. When the joint displacement function is used as the objective function, posture prediction is a MOO problem. The weighted-sum method is commonly used to find a Pareto solution of this MOO problem. Within the joint displacement function, the relative value of the weights associated with each joint represents the relative importance of that joint. Usually, weights are determined by trial and error approaches. This paper presents a systematic approach via an inverse optimization approach to determine the weights for the joint displacement function in posture prediction. This inverse optimization problem can be formulated as a bi-level optimization problem. The design variables are joint angles and weights. The cost function is the summation of the differences between two set of joint angles (the design variables and the realistic posture). Constraints include (1) normalized weights within limits and (2) an inner optimization problem to solve for joint angles (predicted posture). Additional constraints such as weight limits and weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 24 degree of freedom human upper body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected to run the experiment. The set of weights for the general seated posture prediction is obtained by averaging all weights for all subjects and all tasks. On the basis of obtained set of weights, the predicted postures match the experimental results well.

A New MTSA and its Application for Model Optimization

2013 ◽  
Vol 325-326 ◽  
pp. 1258-1261 ◽  
Author(s):  
Jie Chen ◽  
Ling Zhou
Keyword(s):  
Steady State ◽  
Objective Function ◽  
Optimization Problem ◽  
Model Optimization ◽  
Single Output ◽  
Steady State Model ◽  
On Line ◽  
Simulation Results ◽  
Step Algorithm ◽  
Searching Method

In this paper, a modified two-step algorithm (MTSA) is applied to an optimization problem in which the objective function is a multi-input single-output nonlinear steady-state model. An on-line searching method for gain-keeping in MTSA is proposed. The simulation results show that the method is effective.<strong></strong>

Sign in / Sign up

Export Citation Format

Share Document