Memory-based Motion Optimization for Unbounded Resolution

2012 ◽  
Author(s):  
Alan L. Jennings ◽  
Raúl Ordóñez
Keyword(s):  
Motion Optimization

A Dynamics-Based Optimal Trajectory Generation for Controlling an Automated Excavator

2010 ◽  
Vol 224 (10) ◽  
pp. 2109-2119 ◽  
Author(s):  
S Yoo ◽  
C-G Park ◽  
S-H You ◽  
B Lim
Keyword(s):  
Optimal Trajectory ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Constraints ◽  
Control Points ◽  
Weighting Functions ◽  
B Splines ◽  
Finite Dimensional ◽  
Motion Optimization ◽  
Save Energy

This article presents a new methodology to generate optimal trajectories in controlling an automated excavator. By parameterizing all the actuator displacements with B-splines of the same order and with the same number of control points, the coupled actuator limits, associated with the maximum pump flowrate, are described as the finite-dimensional set of linear constraints to the motion optimization problem. Several weighting functions are introduced on the generalized actuator torque so that the solution to each optimization problems contains the physical meaning. Numerical results showing that the generated motions of the excavator are fairly smooth and effectively save energy, which can prevent mechanical wearing and possibly save fuel consumption, are presented. A typical operator's manoeuvre from experiments is referred to bring out the standing features of the optimized motion.

scholarly journals Elasticity-Aware Online Motion Optimization for Link-Elastic Manipulators

2020 ◽  
Vol 53 (2) ◽  
pp. 9980-9985
Author(s):  
Maximilian Krmer ◽  
Freia I. Muster ◽  
Christoph Rsmann ◽  
Torsten Bertram
Keyword(s):  
Motion Optimization

Prediction of Human Motion with Motion Optimization and Neural Networks

2021 ◽  
Author(s):  
Juxing Wang ◽  
Zaojun Fang ◽  
Linyong Shen ◽  
Chen He
Keyword(s):  
Neural Networks ◽  
Human Motion ◽  
Motion Optimization

scholarly journals Ions motion optimization algorithm for multiobjective optimization problems

2021 ◽  
pp. 93-110 ◽  
Author(s):  
Hitarth Buch ◽  
Indrajit Trivedi
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Computational Time ◽  
Selection Strategy ◽  
Pareto Optimum ◽  
Optimal Solutions ◽  
Good Convergence ◽  
Motion Optimization ◽  
Multiobjective Approach ◽  
Benchmark Test Functions

This paper offers a novel multiobjective approach – Multiobjective Ions Motion Optimization (MOIMO) algorithm stimulated by the movements of ions in nature. The main inspiration behind this approach is the force of attraction and repulsion between anions and cations. A storage and leader selection strategy is combined with the single objective Ions Motion Optimization (IMO) approach to estimate the Pareto optimum front for multiobjective optimization. The proposed method is applied to 18 different benchmark test functions to confirm its efficiency in finding optimal solutions. The outcomes are compared with three novel and well-accepted techniques in the literature using five performance parameters quantitatively and obtained Pareto fronts qualitatively. The comparison proves that MOIMO can approximate Pareto optimal solutions with good convergence and coverage with minimum computational time.

Sign in / Sign up

Export Citation Format

Share Document