scholarly journals Two-Dimensional Symmetric Box Delivery Motion Prediction and Validation: Subtask-Based Optimization Method

2020 ◽  
Vol 10 (24) ◽  
pp. 8798
Author(s):  
Yujiang Xiang ◽  
Shadman Tahmid ◽  
Paul Owens ◽  
James Yang
Keyword(s):  
Cost Function ◽  
Material Handling ◽  
Optimization Method ◽  
Joint Torque ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Inverse Dynamic ◽  
Manual Material Handling ◽  
Research Outcome ◽  
The Cost

Box delivery is a complicated manual material handling task which needs to consider the box weight, delivering speed, stability, and location. This paper presents a subtask-based inverse dynamic optimization formulation for determining the two-dimensional (2D) symmetric optimal box delivery motion. For the subtask-based formulation, the delivery task is divided into five subtasks: lifting, the first transition step, carrying, the second transition step, and unloading. To render a complete delivering task, each subtask is formulated as a separate optimization problem with appropriate boundary conditions. For carrying and lifting subtasks, the cost function is the sum of joint torque squared. In contrast, for transition subtasks, the cost function is the combination of joint discomfort and joint torque squared. Joint angle profiles are validated through experimental results using Pearson’s correlation coefficient (r) and root-mean-square-error (RMSE). Results show that the subtask-based approach is computationally efficient for complex box delivery motion simulation. This research outcome provides a practical guidance to prevent injury risks in joint torque space for workers who deliver heavy objects in their daily jobs.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Convergence of Simulated Annealing with Feedback Temperature Schedules

1997 ◽  
Vol 11 (3) ◽  
pp. 279-304 ◽  
Author(s):  
M. Kolonko ◽  
M. T. Tran
Keyword(s):  
Simulated Annealing ◽  
Cost Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Search Process ◽  
Local Optimum ◽  
Fixed Sequence ◽  
Temperature Parameter ◽  
The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

scholarly journals Formulating and Deploying Strength Amplification Controllers for Lower-Body Walking Exoskeletons

2021 ◽  
Vol 8 ◽  
Author(s):  
Gray C. Thomas ◽  
Orion Campbell ◽  
Nick Nichols ◽  
Nicolas Brissonneau ◽  
Binghan He ◽  
...  
Keyword(s):  
Material Handling ◽  
Task Force ◽  
Inequality Constraints ◽  
Whole Body ◽  
Lower Body ◽  
Computationally Efficient ◽  
Physical Strength ◽  
Manual Material Handling ◽  
Control Framework ◽  
Machine Interface

Augmenting the physical strength of a human operator during unpredictable human-directed (volitional) movements is a relevant capability for several proposed exoskeleton applications, including mobility augmentation, manual material handling, and tool operation. Unlike controllers and augmentation systems designed for repetitive tasks (e.g., walking), we approach physical strength augmentation by a task-agnostic method of force amplification—using force/torque sensors at the human–machine interface to estimate the human task force, and then amplifying it with the exoskeleton. We deploy an amplification controller that is integrated into a complete whole-body control framework for controlling exoskeletons that includes human-led foot transitions, inequality constraints, and a computationally efficient prioritization. A powered lower-body exoskeleton is used to demonstrate behavior of the control framework in a lab environment. This exoskeleton can assist the operator in lifting an unknown backpack payload while remaining fully backdrivable.

scholarly journals Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4362
Author(s):  
Subramaniam Saravana Sankar ◽  
Yiqun Xia ◽  
Julaluk Carmai ◽  
Saiprasit Koetniyom
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Fuel Consumption ◽  
Optimization Algorithm ◽  
Internal Combustion Engines ◽  
Optimization Method ◽  
Computational Time ◽  
Weight Factor ◽  
Driving Cycles ◽  
The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

Optimum Trajectory Planning for Redundant and Hyper-Redundant Manipulators Through Inverse Dynamics

2011 ◽  
Author(s):  
Kagan K. Ayten ◽  
M. Necip Sahinkaya ◽  
P. Iravani
Keyword(s):  
Cost Function ◽  
Trajectory Planning ◽  
Inverse Dynamics ◽  
Minimum Energy ◽  
Redundant Manipulators ◽  
Inverse Dynamic ◽  
Dynamic Constraints ◽  
Function Calculation ◽  
The Cost ◽  
Fifth Order

This paper presents a method to develop minimum energy trajectories for redundant/hyper-redundant manipulators with pre-defined kinematic and dynamic constraints. The optimal trajectory planning uses fifth order B-spline functions to represent the Cartesian coordinates of the end-effector and angles of the redundant links. The actuator torques are considered for the formulation of the cost function. Calculation of the cost function is carried out by using an inverse dynamic analysis. The system constraints are handled within the cost function to avoid running the inverse dynamics when the constraints are not satisfied. A novel virtual link concept is introduced to replace all the redundant links to eliminate physicaly impossible configurations before running the inverse dynamic model. The process is applicable to hyper redundant manipulators with large number of links. The proposed scheme is verified with computer simulations based on a 5-link planar redundant manipulator.

scholarly journals An Effective Optimization Method for Machine Learning Based on ADAM

2020 ◽  
Vol 10 (3) ◽  
pp. 1073 ◽  
Author(s):  
Dokkyun Yi ◽  
Jaehyun Ahn ◽  
Sangmin Ji
Keyword(s):  
Machine Learning ◽  
Cost Function ◽  
Gradient Descent ◽  
Local Minimum ◽  
Optimization Method ◽  
Activation Function ◽  
Numerical Comparison ◽  
First Derivative ◽  
Artificial Neural Network Ann ◽  
The Cost

A machine is taught by finding the minimum value of the cost function which is induced by learning data. Unfortunately, as the amount of learning increases, the non-liner activation function in the artificial neural network (ANN), the complexity of the artificial intelligence structures, and the cost function’s non-convex complexity all increase. We know that a non-convex function has local minimums, and that the first derivative of the cost function is zero at a local minimum. Therefore, the methods based on a gradient descent optimization do not undergo further change when they fall to a local minimum because they are based on the first derivative of the cost function. This paper introduces a novel optimization method to make machine learning more efficient. In other words, we construct an effective optimization method for non-convex cost function. The proposed method solves the problem of falling into a local minimum by adding the cost function in the parameter update rule of the ADAM method. We prove the convergence of the sequences generated from the proposed method and the superiority of the proposed method by numerical comparison with gradient descent (GD, ADAM, and AdaMax).

A Novel Redundancy Resolution Method via Optimization with Additional Tasks

2013 ◽  
Vol 694-697 ◽  
pp. 1787-1792
Author(s):  
Xue Feng Zhou
Keyword(s):  
Cost Function ◽  
Optimization Method ◽  
Initial Configuration ◽  
Main Task ◽  
Joint Position ◽  
Redundancy Resolution ◽  
Redundant Manipulator ◽  
Resolution Method ◽  
Additional Task ◽  
The Cost

This paper presents an optimization method for redundant manipulator redundancy resolution with additional task. The cost function is a compromise between the requirement of accuracy of the main task, the accuracy of the additional task, and the feasibility of the joint velocities. The joint rates that minimize the cost function can be found, and the joint position trajectories can be integrated with an initial configuration. The effectiveness of this presented method is verified by a planar 3-DoF PRR manipulator.

scholarly journals The reserve of joint torque determines movement coordination

2021 ◽  
Author(s):  
Germain Faity ◽  
Denis Mottet ◽  
Simon Pla ◽  
Jérôme Froger
Keyword(s):  
Optimal Control ◽  
Cost Function ◽  
Degrees Of Freedom ◽  
Minimum Cost ◽  
Joint Torque ◽  
Movement Coordination ◽  
Trunk Motion ◽  
Additional Load ◽  
The Cost ◽  
Healthy Participants

AbstractHumans coordinate biomechanical degrees of freedom to perform tasks at minimum cost. When reaching a target from a seated position, the trunk-arm-forearm coordination moves the hand to the well-defined spatial goal, while typically minimising hand jerk and trunk motion. However, due to fatigue or stroke, people visibly move the trunk more, and it is unclear what cost can account for this. Here we show that people recruit their trunk when the torque at the shoulder is too close to the maximum. We asked 26 healthy participants to reach a target while seated and we found that the trunk contribution to hand displacement increases from 11% to 27% when an additional load is handled. By flexing and rotating the trunk, participants spontaneously increase the reserve of anti-gravitational torque at the shoulder from 25% to 40% of maximal voluntary torque. Our findings provide hints on how to include the reserve of torque in the cost function of optimal control models of human coordination in healthy fatigued persons or in stroke victims.

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