scholarly journals State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

10.5772/50918 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 23 ◽  
Author(s):  
Xuyang Wang ◽  
Tiansheng Lu ◽  
Peiyan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Humanoid Robots ◽  
The State ◽  
Stable States ◽  
New Approach ◽  
Motion Data ◽  
Reference Motion ◽  
State Generation ◽  
Original Motion

A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.

State Forecasting for Rotary Machine Based on Neural Network and Genetic Algorithm

2007 ◽  
Author(s):  
Hongmei Liu ◽  
Shaoping Wang ◽  
Pingchao Ouyang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The State ◽  
Structure Parameters ◽  
Hydraulic Pump ◽  
Bp Network ◽  
New Approach ◽  
Rotary Machine ◽  
Optimal Network ◽  
Forecasting Method

A state forecasting is a key technology to achieve the advanced predictive maintenance. A Prediction based on neural network is a new approach to realize the state predicting. The present neural networks predicting models are comparatively poor in adaptability to environment and in predicting accuracy, therefore, a new rotary machine online state forecasting method based on the genetic algorithm (GA) and neural network (NN) was presented. GA was used for dynamical optimizing the structure parameters of BP network to obtain the optimal network structure. A training algorithm combining GA with BP was adopted to avoid the local minimum and to heighten the learning precision. The state predicting results for hydraulic pump indicate that the predicting model purposed may dynamically optimize the structure parameters in accordance with different conditions, and gained satisfactory results.

When the State Speaks, What Should It Say?

2012 ◽  
Author(s):  
Corey Brettschneider
Keyword(s):  
Freedom Of Expression ◽  
The State ◽  
Hate Groups ◽  
New Approach ◽  
State Action ◽  
Democratic State ◽  
Theory Of Value ◽  
The Right ◽  
Equal Citizenship ◽  
The Ideal

How should a liberal democracy respond to hate groups and others that oppose the ideal of free and equal citizenship? The democratic state faces the hard choice of either protecting the rights of hate groups and allowing their views to spread, or banning their views and violating citizens' rights to freedoms of expression, association, and religion. Avoiding the familiar yet problematic responses to these issues, this book proposes a new approach called value democracy. The theory of value democracy argues that the state should protect the right to express illiberal beliefs, but the state should also engage in democratic persuasion when it speaks through its various expressive capacities: publicly criticizing, and giving reasons to reject, hate-based or other discriminatory viewpoints. Distinguishing between two kinds of state action—expressive and coercive—the book contends that public criticism of viewpoints advocating discrimination based on race, gender, or sexual orientation should be pursued through the state's expressive capacities as speaker, educator, and spender. When the state uses its expressive capacities to promote the values of free and equal citizenship, it engages in democratic persuasion. By using democratic persuasion, the state can both respect rights and counter hateful or discriminatory viewpoints. The book extends this analysis from freedom of expression to the freedoms of religion and association, and shows that value democracy can uphold the protection of these freedoms while promoting equality for all citizens.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

scholarly journals A New Approach of Soft Joint Based on a Cable-Driven Parallel Mechanism for Robotic Applications

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1468
Author(s):  
Luis Nagua ◽  
Carlos Relaño ◽  
Concepción A. Monje ◽  
Carlos Balaguer
Keyword(s):  
Parallel Mechanism ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Experimental Tests ◽  
Humanoid Robots ◽  
Inverse Kinematic ◽  
Element Analysis ◽  
New Approach ◽  
Flexible Polymer ◽  
The Mathematical Model

A soft joint has been designed and modeled to perform as a robotic joint with 2 Degrees of Freedom (DOF) (inclination and orientation). The joint actuation is based on a Cable-Driven Parallel Mechanism (CDPM). To study its performance in more detail, a test platform has been developed using components that can be manufactured in a 3D printer using a flexible polymer. The mathematical model of the kinematics of the soft joint is developed, which includes a blocking mechanism and the morphology workspace. The model is validated using Finite Element Analysis (FEA) (CAD software). Experimental tests are performed to validate the inverse kinematic model and to show the potential use of the prototype in robotic platforms such as manipulators and humanoid robots.

MOTION PLANNING USING PREDICTED PERCEPTIVE CAPABILITY

2009 ◽  
Vol 06 (03) ◽  
pp. 435-457 ◽  
Author(s):  
PHILIPP MICHEL ◽  
JOEL CHESTNUTT ◽  
SATOSHI KAGAMI ◽  
KOICHI NISHIWAKI ◽  
JAMES J. KUFFNER ◽  
...  
Keyword(s):  
Motion Planning ◽  
Planning Process ◽  
Humanoid Robots ◽  
Experimental Results ◽  
Success Rates ◽  
Perception System ◽  
Task Success

We present an approach to motion planning for humanoid robots that aims to ensure reliable execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By efficiently simulating the robot's perception system during search, our planner utilizes a perceptive capability metric that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous grasping motions and walking sequences as performed by an HRP-2 humanoid. A fast GPU-accelerated 3D tracker is used for perception, with a grasp planner and footstep planner incorporating reasoning about the robot's perceptive capability. Experimental results show that considering information about the predicted perceptive capability ensures that sensing remains operational throughout the grasping or walking sequence and yields higher task success rates than perception-unaware planning.

Forecasting State Income Tax Collections: A New Approach

1978 ◽  
Vol 6 (1) ◽  
pp. 67-91 ◽  
Author(s):  
David M. Reaume
Keyword(s):  
North Carolina ◽  
Income Distribution ◽  
Income Tax ◽  
The State ◽  
New Approach ◽  
State Income Tax ◽  
State Income ◽  
Complete Detail ◽  
The Law

This paper reports on an application of the microsimulation method to the estimation of income tax collections for the State of North Carolina. Detailed forecasts of Income distribution make it Possible to model the law in nearly complete detail. The model provides quarterly forecasts of collections disaggregated by withheld taxes, declarations payments, final payments, and refunds.

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

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