Natural motion generation of biped locomotion robot using hierarchical trajectory generation method consisting of GA, EP layers

2002 ◽  
Author(s):  
T. Arakawa ◽  
T. Fukuda
Keyword(s):  
Trajectory Generation ◽  
Biped Locomotion ◽  
Motion Generation ◽  
Natural Motion

Natural Motion Generation of Biped Locomotion Robot using Hierarchical Evolutionary Algorithm in the Various Environments

1997 ◽  
Vol 9 (6) ◽  
pp. 496-502
Author(s):  
Takemasa Arakawa ◽  
◽  
Toshio Fukuda
Keyword(s):  
Evolutionary Algorithm ◽  
Energy Minimization ◽  
Dynamic Effect ◽  
The Other ◽  
Human Walking ◽  
Motion Trajectory ◽  
Biped Locomotion ◽  
Motion Generation ◽  
Natural Motion ◽  
Generation Problem

The purpose of this research is to generate natural motion in a biped locomotion robot, like a human walking in various environments. In this paper, we report on biped locomotion robots. We apply a hierarchical evolutionary algorithm in order to generate the trajectory of a biped locomotion robot through energy optimization, and attempt to generate a more natural motion by considering the dynamic effect. The hierarchical evolutionary algorithm consists of two layers: one is the GA layer which minimizes the total energy of all the actuators, and the other is the EP layer which optimizes the interpolated configuration of the biped locomotion robot. Then we formulate a trajectory generation problem as an energy minimization problem and we apply the hierarchical evolutionary algorithm. Furthermore, we build a trial biped locomotion robot which has 13 joints and is made of aluminum. Finally, we confirm that the calculated natural motion trajectory can be applied successfully to practical biped locomotion.

TRAJECTORY GENERATION BETWEEN TWO ARBITRARY STATES BASED ON HAMILTON'S PRINCIPLE A VARIABLE SUBSTITUTION METHOD

2012 ◽  
Vol 09 (03) ◽  
pp. 1250023 ◽  
Author(s):  
SUSUMU MORITA
Keyword(s):  
Lagrange Equation ◽  
Trajectory Generation ◽  
Modern Science ◽  
Mathematical Explanation ◽  
Hamilton’S Principle ◽  
Human Beings ◽  
Motion Generation ◽  
Hamilton's Principle ◽  
Variable Substitution ◽  
Human Motions

Motion principles of animals and humans has been a field of interest for over half a century. As for human motions, the study by modern science started off by a Soviet physiologist in the 1930's, followed by many proposals of models and principles to explain how human beings move. This paper introduces an alternative motion principle with a motion generation method. The subject in discussion is the Hamilton's principle which yields equation of free motion. The motion generation method is derived based on a novel variable substitution to be applied to the Hamilton's principle which then yields the Euler–Lagrange equation that can be used for motion trajectory generation between two arbitrary states. A numerical example with measured data is shown, and a mathematical explanation of the variable substitution and the qualitative meaning of the trajectory generation method is given.

Trajectory generation for biped locomotion robot

Mechatronics ◽  
2000 ◽  
Vol 10 (1-2) ◽  
pp. 67-89 ◽  
Author(s):  
Yasuhisa Hasegawa ◽  
Takemasa Arakawa ◽  
Toshio Fukuda
Keyword(s):  
Trajectory Generation ◽  
Biped Locomotion

scholarly journals Euclidean metrics for motion generation on SE(3)

2002 ◽  
Vol 216 (1) ◽  
pp. 47-60 ◽  
Author(s):  
C Belta ◽  
V Kumar
Keyword(s):  
Ad Hoc ◽  
Trajectory Generation ◽  
Rigid Bodies ◽  
Second Step ◽  
Computationally Efficient ◽  
Motion Generation ◽  
Euclidean Group ◽  
Screw Motions ◽  
Euclidean Metrics ◽  
Generation Problem

Previous approaches to trajectory generation for rigid bodies have been either based on the so-called invariant screw motions or on ad hoc decompositions into rotations and translations. This paper formulates the trajectory generation problem in the framework of Lie groups and Riemannian geometry. The goal is to determine optimal curves joining given points with appropriate boundary conditions on the Euclidean group. Since this results in a two-point boundary value problem that has to be solved iteratively, a computationally efficient, analytical method that generates near-optimal trajectories is derived. The method consists of two steps. The first step involves generating the optimal trajectory in an ambient space, while the second step is used to project this trajectory onto the Euclidean group. The paper describes the method, its applications and its performance in terms of optimality and efficiency.

scholarly journals MC_MoveAbsolute() 4th Order Real-Time Trajectory Generation Function Algorithm and Implementation

2019 ◽  
Vol 9 (3) ◽  
pp. 538
Author(s):  
Krzysztof Pietrusewicz ◽  
Paweł Waszczuk ◽  
Michał Kubicki
Keyword(s):  
Motion Control ◽  
Code Generation ◽  
Trajectory Generation ◽  
Theoretical Aspect ◽  
Practical Implementation ◽  
Motion Generation ◽  
Servo Drive ◽  
Optimal Point ◽  
Time Optimal ◽  
The Individual

This paper presents the issue of generating motion trajectories in a digital servo drive in accordance with the PLCopen Motion Control standard. This standard does not limit the details of motion generation in the electromechanical systems, but indicates its interface and set of necessary parameters. Moreover, it is placed within a state machine, which allows the individual software elements to integrate with it seamlessly. This work discusses time-optimal point-to-point trajectories, i.e., the initial and final reference speeds are zero, and they are compliant with the MC_MoveAbsolute() function defined in the PLCopen Motion Control standard. The smoothness of the resulting trajectory can be attributed to the use of a fourth order trajectory generator, which defines the bounds up to snap – the second derivative of acceleration. One of the aims of this article was to bridge the theoretical aspect of trajectory generation with the algorithms practical implementation, by the means of PLC code generation using the MATLAB/Simulink package.

Longitudinal Front Wheel Drive Vehicle Maneuvers Considering DC-Motors Constraints

1999 ◽  
Author(s):  
Yasmina Bestaoui
Keyword(s):  
Objective Function ◽  
Trajectory Generation ◽  
Front Wheel ◽  
Maximum Power ◽  
Motion Generation ◽  
The Road ◽  
Dc Motors ◽  
Wheel Drive ◽  
Actuator Constraints

Abstract This paper is concerned with trajectory generation of vehicles taking into account the vehicle’s dynamics and the actuators’ constraints in voltages and currents. Motion generation is formulated as an optimisation problem with a mixed energy-time objective function. Usual kinematics constraints are not sufficiently representative of the reality, thus we focus on more realistic constraints: on the acceleration, the jerk, the maximum power which can be transferred to the road and voltage and current DC actuator constraints.

Sign in / Sign up

Export Citation Format

Share Document