State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

10.5772/5768 ◽  
2008 ◽  
pp. 1 ◽  
Author(s):  
Xuyang Wang ◽  
Tiansheng Lu ◽  
Peiyan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
State Generation

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.

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.

Optimized inchworm motion planning for a novel in-pipe robot

2013 ◽  
Vol 228 (7) ◽  
pp. 1248-1258 ◽  
Author(s):  
Qingyou Liu ◽  
Yonghua Chen ◽  
Tao Ren ◽  
Ying Wei
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Strain Energy ◽  
Modern Society ◽  
Robot Design ◽  
Stroke Length ◽  
Velocity Pattern ◽  
Optimization Result ◽  
Gradient Based ◽  
Inspection And Maintenance

Modern society is fueled by very comprehensive grids of gas and liquid supply pipelines. The frequent inspection and maintenance of such pipeline grids is not a trivial task. It has been demonstrated that such task is best performed by using in-pipe robots. In this paper, a novel inchworm robot design and its optimized motion planning are presented. The proposed design uses a helical drive for both gripping and locomotion of the robot. The extension and retraction between inchworm segments are facilitated by conic springs as they can store strain energy. The proposed inchworm robot can also be made very compact without sacrificing stroke length as conic springs can be easily designed with telescopic feature. To generate inchworm motion, a sinusoidal velocity pattern is planned for each segment. The frequency of the velocity pattern is optimized using a genetic algorithm (GA). The optimization result from the GA method has been validated using a traditional gradient based method.

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